Expanding attribute profiles

ABSTRACT

A method and system for expanding attribute profiles are presented in which primary attributes from one or more attribute profiles are used to derive secondary attributes which are added to the respective attribute profiles to generate expanded attribute profiles. The expanded attribute profiles are generated to increase the strength of association of a query attribute with one or more attribute profiles associated with query-attribute-positive individuals.

This application claims priority to U.S. Provisional Application Ser.No. 60/895,236, which was filed on Mar. 16, 2007, and which isincorporated herein by reference in its entirety.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description will be better understood when readin conjunction with the appended drawings, in which there is shown oneor more of the multiple embodiments of the present invention. It shouldbe understood, however, that the various embodiments are not limited tothe precise arrangements and instrumentalities shown in the drawings.

FIG. 1 illustrates attribute categories and their relationships;

FIG. 2 illustrates a system diagram including data formatting,comparison, and statistical computation engines and dataset input/outputfor a method of creating an attribute combinations database;

FIG. 3 illustrates examples of genetic attributes;

FIG. 4 illustrates examples of epigenetic attributes;

FIG. 5 illustrates representative physical attributes classes;

FIG. 6 illustrates representative situational attributes classes;

FIG. 7 illustrates representative behavioral attributes classes;

FIG. 8 illustrates an attribute determination system;

FIG. 9 illustrates an example of expansion and reformatting ofattributes;

FIG. 10 illustrates the advantage of identifying attribute combinationsin a two attribute example;

FIG. 11 illustrates the advantage of identifying attribute combinationsin a three attribute example;

FIG. 12 illustrates an example of statistical measures & formulas usefulfor the methods;

FIG. 13 illustrates a flow chart for a method of creating an attributecombinations database;

FIG. 14 illustrates a 1st dataset example for a method of creating anattribute combinations database;

FIG. 15 illustrates 2nd dataset and combinations table examples for amethod of creating an attribute combinations database;

FIG. 16 illustrates a 3rd dataset example for a method of creating anattribute combinations database;

FIG. 17 illustrates a 4th dataset example for a method of creating anattribute combinations database;

FIG. 18 illustrates a 4th dataset example for a method of creating anattribute combinations database;

FIG. 19 illustrates a flowchart for a method of identifying predisposingattribute combinations;

FIG. 20 illustrates a rank-ordered tabulated results example for amethod of identifying predisposing attribute combinations;

FIG. 21 illustrates a flowchart for a method of predispositionprediction;

FIG. 22 illustrates 1st and 2nd dataset examples for a method ofpredisposition prediction;

FIG. 23 illustrates 3rd dataset and tabulated results examples for amethod of predisposition prediction;

FIG. 24 illustrates a flowchart for a method of destiny modification;

FIG. 25 illustrates 1st dataset, 3rd dataset and tabulated resultsexamples for destiny modification of individual #113;

FIG. 26 illustrates 1st dataset, 3rd dataset and tabulated resultsexamples for destiny modification of individual #114;

FIG. 27 illustrates 3rd dataset examples from a method of destinymodification for use in synergy discovery;

FIG. 28 illustrates one embodiment of a computing system on which thepresent method and system can be implemented; and

FIG. 29 illustrates a representative deployment diagram for an attributedetermination system.

DETAILED DESCRIPTION

Described herein are methods, computer systems, databases and softwarefor identifying combinations of attributes associated with individualsthat co-occur with key attributes, such as specific disorders, behaviorsand traits. Also described are databases as well as database systems forcreating and accessing databases describing those attributes and forperforming analyses based on those attributes. The methods, computersystems and software are useful for identifying intricate combinationsof attributes that predispose human beings toward having or developingspecific disorders, behaviors and traits of interest, determining thelevel of predisposition of an individual towards such attributes, andrevealing which attribute associations can be added or eliminated toeffectively modify what may have been hereto believed to be destiny. Themethods, computer systems and software are also applicable for tissuesand non-human organisms, as well as for identifying combinations ofattributes that correlate with or cause behaviors and outcomes incomplex non-living systems including molecules, electrical andmechanical systems and various devices and apparatus whose functionalityis dependent on a multitude of attributes.

Previous methods have been largely unsuccessful in determining thecomplex combinations of attributes that predispose individuals to mostdisorders, behaviors and traits. The level of resolution afforded by thedata typically used is too low, the number and types of attributesconsidered is too limited, and the sensitivity to detect low frequency,high complexity combinations is lacking. The desirability of being ableto determine the complex combinations of attributes that predispose anindividual to physical or behavioral disorders has clear implicationsfor improving individualized diagnoses, choosing the most effectivetherapeutic regimens, making beneficial lifestyle changes that preventdisease and promote health, and reducing associated health careexpenditures. It is also desirable to determine those combinations ofattributes that promote certain behaviors and traits such as success insports, music, school, leadership, career and relationships.

FIG. 1 displays one embodiment of the attribute categories and theirinterrelationships according to the present invention and illustratesthat physical and behavioral attributes can be collectively equivalentto the broadest classical definition of phenotype, while situationalattributes can be equivalent to those typically classified asenvironmental. In one embodiment, historical attributes can be viewed asa separate category containing a mixture of genetic, epigenetic,physical, behavioral and situational attributes that occurred in thepast. Alternatively, historical attributes can be integrated within thegenetic, epigenetic, physical, behavioral and situational categoriesprovided they are made readily distinguishable from those attributesthat describe the individual's current state. In one embodiment, thehistorical nature of an attribute is accounted for via a time stamp orother time based marker associated with the attribute. As such, thereare no explicit historical attributes, but through use of time stamping,the time associated with the attribute can be used to make adetermination as to whether the attribute is occurring in what would beconsidered the present, or if it has occurred in the past. Traditionaldemographic factors are typically a small subset of attributes derivedfrom the phenotype and environmental categories and can be thereforerepresented within the physical, behavioral and situational categories.

In the present invention the term ‘attributes’ rather than the term‘factors’ is used since many of the entities are characteristicsassociated with an individual that may have no influence on the vastmajority of their traits, behaviors and disorders. As such, there may bemany instances during execution of the methods described herein when aparticular attribute does not act as a factor in determiningpredisposition. Nonetheless, every attribute remains a potentiallyimportant characteristic of the individual and may contribute topredisposition toward some other attribute or subset of attributesqueried during subsequent or future implementation of the methodsdescribed herein. An individual possesses many associated attributeswhich may be collectively referred to as an attribute profile associatedwith that individual. In one embodiment, an attribute profile can beconsidered as being comprised of the attributes that are present (i.e.,occur) in that profile, as well as being comprised of the variouscombinations (i.e., combinations and subcombinations) of thoseattributes. The attribute profile of an individual is preferablyprovided to embodiments of the present invention as a dataset recordwhose association with the individual can be indicated by a uniqueidentifier contained in the dataset record. An actual attribute of anindividual can be represented by an attribute descriptor in attributeprofiles, records, datasets, and databases. Herein, both actualattributes and attribute descriptors may be referred to simply asattributes. In one embodiment, statistical relationships andassociations between attribute descriptors are a direct result ofrelationships and associations between actual attributes of anindividual. In the present disclosure, the term ‘individual’ can referto a singular group, person, organism, organ, tissue, cell, virus,molecule, thing, entity or state, wherein a state includes but is notlimited to a state-of-being, an operational state or a status.Individuals, attribute profiles and attributes can be real and/ormeasurable, or they may be hypothetical and/or not directly observable.

In one embodiment the present invention can be used to discovercombinations of attributes regardless of number or type, in a populationof any size, that cause predisposition to an attribute of interest. Indoing so, this embodiment also has the ability to provide a list ofattributes one can add or subtract from an existing profile ofattributes in order to respectively increase or decrease the strength ofpredisposition toward the attribute of interest. The ability toaccurately detect predisposing attribute combinations naturally benefitsfrom being supplied with datasets representing large numbers ofindividuals and having a large number and variety of attributes foreach. Nevertheless, the present invention will function properly with aminimal number of individuals and attributes. One embodiment of thepresent invention can be used to detect not only attributes that have adirect (causal) effect on an attribute of interest, but also thoseattributes that do not have a direct effect such as instrumentalvariables (i.e., correlative attributes), which are attributes thatcorrelate with and can be used to predict predisposition for theattribute of interest but are not causal. For simplicity of terminology,both types of attributes are referred to herein as predisposingattributes, or simply attributes, that contribute toward predispositiontoward the attribute of interest, regardless of whether the contributionor correlation is direct or indirect.

It is beneficial, but not necessary, in most instances, that theindividuals whose data is supplied for the method be representative ofthe individual or population of individuals for which the predictionsare desired. In a preferred embodiment, the attribute categoriescollectively encompass all potential attributes of an individual. Eachattribute of an individual can be appropriately placed in one or moreattribute categories of the methods, system and software of theinvention. Attributes and the various categories of attributes can bedefined as follows:

-   -   a) attribute: a quality, trait, characteristic, relationship,        property, factor or object associated with or possessed by an        individual;    -   b) genetic attribute: any genome, genotype, haplotype,        chromatin, chromosome, chromosome locus, chromosomal material,        deoxyribonucleic acid (DNA), allele, gene, gene cluster, gene        locus, gene polymorphism, gene mutation, gene marker,        nucleotide, single nucleotide polymorphism (SNP), restriction        fragment length polymorphism (RFLP), variable tandem repeat        (VTR), genetic marker, sequence marker, sequence tagged site        (STS), plasmid, transcription unit, transcription product,        ribonucleic acid (RNA), and copy DNA (cDNA), including the        nucleotide sequence and encoded amino acid sequence of any of        the above;    -   c) epigenetic attribute: any feature of the genetic material—all        genomic, vector and plasmid DNA, and chromatin—that affects gene        expression in a manner that is heritable during somatic cell        divisions and sometimes heritable in germline transmission, but        that is nonmutational to the DNA sequence and is therefore        fundamentally reversible, including but not limited to        methylation of DNA nucleotides and acetylation of        chromatin-associated histone proteins;    -   d) pangenetic attribute: any genetic or epigenetic attribute;    -   e) physical attribute: any material quality, trait,        characteristic, property or factor of an individual present at        the atomic, molecular, cellular, tissue, organ or organism        level, excluding genetic and epigenetic attributes;    -   f) behavioral attribute: any singular, periodic, or aperiodic        response, action or habit of an individual to internal or        external stimuli, including but not limited to an action,        reflex, emotion or psychological state that is controlled or        created by the nervous system on either a conscious or        subconscious level;    -   g) situational attribute: any object, condition, influence, or        milieu that surrounds, impacts or contacts an individual; and    -   h) historical attribute: any genetic, epigenetic, physical,        behavioral or situational attribute that was associated with or        possessed by an individual in the past. As such, the historical        attribute refers to a past state of the individual and may no        longer describe the current state.

The methods, systems, software, and databases described herein apply toand are suitable for use with not only humans, but for other organismsas well. The methods, systems, software and databases may also be usedfor applications that consider attribute identification, predispositionpotential and destiny modification for organs, tissues, individualcells, and viruses both in vitro and in vivo. For example, the methodscan be applied to behavior modification of individual cells being grownand studied in a laboratory incubator by providing pangenetic attributesof the cells, physical attributes of the cells such as size, shape andsurface receptor densities, and situational attributes of the cells suchas levels of oxygen and carbon dioxide in the incubator, temperature ofthe incubator, and levels of glucose and other nutrients in the liquidgrowth medium. Using these and other attributes, the methods, systems,software and databases can then be used to predict predisposition of thecells for such characteristics as susceptibility to infection byviruses, general growth rate, morphology, and differentiation potential.The methods, systems, software, and databases described herein can alsobe applied to complex non-living systems to, for example, predict thebehavior of molecules or the performance of electrical devices ormachinery subject to a large number of variables.

FIG. 2 illustrates system components corresponding to one embodiment ofa method, system, software, and databases for compiling predisposingattribute combinations. Attributes can be stored in the various datasetsof the system. In one embodiment, 1st dataset 200 is a raw dataset ofattributes that may be converted and expanded by conversion/formattingengine 220 into a more versatile format and stored in expanded 1stdataset 202. Comparison engine 222 can perform a comparison betweenattributes from records of the 1st dataset 200 or expanded 1st dataset202 to determine candidate predisposing attributes which are then storedin 2nd dataset 204. Comparison engine 222 can tabulate a list of allpossible combinations of the candidate attributes and then perform acomparison of those combinations with attributes contained withinindividual records of 1st dataset 200 or expanded 1st dataset 202.Comparison engine 222 can store those combinations that are found tooccur and meet certain selection criteria in 3rd dataset 206 along witha numerical frequency of occurrence obtained as a count during thecomparison. Statistical computation engine 224 can perform statisticalcomputations using the numerical frequencies of occurrence to obtainresults for strength of association between attributes and attributecombinations and then store those results in 3rd dataset 206.Statistical computation engine 224, alone or in conjunction withcomparison engine 222, can create a 4th dataset 208 containingattributes and attribute combinations that meet a minimum or maximumstatistical requirement by applying a numerical or statistical filter tothe numerical frequencies of occurrence or the results for strength ofassociation stored in 3rd dataset 206. Although represented as a systemand engines, the system and engines can be considered subsystems of alarger system, and as such referred to as subsystems. Such subsystemsmay be implemented as sections of code, objects, or classes of objectswithin a single system, or may be separate hardware and softwareplatforms which are integrated with other subsystems to form the finalsystem.

FIGS. 3A and 3B show a representative form for genetic attributes as DNAnucleotide sequence with each nucleotide position associated with anumerical identifier. In this form, each nucleotide is treated as anindividual genetic attribute, thus providing maximum resolution of thegenomic information of an individual. FIG. 3A depicts a known genesequence for the HTR2A gene. Comparing known genes simplifies the taskof properly phasing nucleotide sequence comparisons. However, forcomparison of non-gene sequences, due to the presence of insertions anddeletions of varying size in the genome of one individual versusanother, markers such as STS sequences can be used to allow for a properin-phase comparison of the DNA sequences between different individuals.FIG. 3B shows DNA plus-strand sequence beginning at the STS#68777forward primer which provides a known location of the sequence withinthe genome and allows phasing of the sequence with other sequences fromthat region of the genome during sequence comparison.

Conversion/formatting engine 220 of FIG. 2 can be used in conjunctionwith comparison engine 222 to locate and number the STS marker positionswithin the sequence data and store the resulting data in expanded 1stdataset 202. In one embodiment, comparison engine 222 has the ability torecognize strings of nucleotides with a word size large enough to enableaccurately phased comparison of individual nucleotides in the spanbetween marker positions. This function is also valuable in comparingknown gene sequences. Nucleotide sequence comparisons in the presentinvention can also involve transcribed sequences in the form of mRNA,tRNA, rRNA, and cDNA sequences which all derive from genomic DNAsequence and are handled in the same manner as nucleotide sequences ofknown genes.

FIGS. 3C and 3D show two other examples of genetic attributes that maybe compared in one embodiment of the present invention and the formatthey may take. Although not preferred because of the relatively smallamount of information provided, SNP polymorphisms (FIG. 3C) and alleleidentity (FIG. 3D) can be processed by one or more of the methods hereinto provide a limited comparison of the genetic content of individuals.

FIGS. 4A and 4B show examples of epigenetic data that can be compared,the preferred epigenetic attributes being methylation site data. FIG. 4Arepresents a format of methylation data where each methylation site(methylation variable position) is distinguishable by a uniquealphanumeric identifier. The identifier may be further associated with aspecific gene, site or chromosomal locus of the genome. In thisembodiment, the methylation status at each site is an attribute that canhave either of two values: methylated (M) or unmethylated (U). Otherepigenetic data and representations of epigenetic data can be used toperform the methods described herein, and to construct the systems,software and databases described herein, as will be understood by oneskilled in the art.

As shown in FIG. 4B, an alternative way to organize the epigenetic datais to append it directly into the corresponding genetic attributedataset in the form of methylation status at each candidate CpGdinucleotide occurring in that genomic nucleotide sequence. Theadvantage of this format is that it inherently includes chromosome, geneand nucleotide position information. In this format, which is the mostcomplete and informative format for the raw data, the epigenetic datacan be extracted and converted to another format at any time. Bothformats (that of FIG. 4A as well as that of FIG. 4B) provide the sameresolution of methylation data, but it is preferable to adhere to oneformat in order to facilitate comparison of epigenetic data betweendifferent individuals. Regarding either data format, in instances wherean individual is completely lacking a methylation site due to a deletionor mutation of the corresponding CpG dinucleotide, the correspondingepigenetic attribute value should be omitted (i.e., assigned a null).

FIG. 5 illustrates representative classes of physical attributes asdefined by physical attributes metaclass 500, which can include physicalhealth class 510, basic physical class 520, and detailed physical class530, for example. In one embodiment physical health class 510 includes aphysical diagnoses subclass 510.1 that includes the following specificattributes (objects), which when positive indicate a known physicaldiagnoses:

510.1.1 Diabetes

510.1.2 Heart Disease

510.1.3 Osteoporosis

510.1.4 Stroke

510.1.5 Cancer

-   -   510.1.5.1 Prostrate Cancer    -   510.1.5.2 Breast Cancer    -   510.1.5.3 Lung Cancer    -   510.1.5.4 Colon Cancer    -   510.1.5.5 Bladder Cancer    -   510.1.5.6 Endometrial Cancer    -   510.1.5.7 Non-Hodgkin's Lymphoma    -   510.1.5.8 Ovarian Cancer    -   510.1.5.9 Kidney Cancer    -   510.1.5.10 Leukemia    -   510.1.5.11 Cervical Cancer    -   510.1.5.12 Pancreatic Cancer    -   510.1.5.13 Skin melanoma    -   510.1.5.14 Stomach Cancer

510.1.6 Bronchitis

510.1.7 Asthma

510.1.8 Emphysema

The above classes and attributes represent the current condition of theindividual. In the event that the individual (e.g. consumer 810) had adiagnosis for an ailment in the past, the same classificationmethodology can be applied, but with an “h” placed after the attributenumber to denote a historical attribute. For example, 510.1.4h can beused to create an attribute to indicate that the individual suffered astroke in the past, as opposed to 510.1.4 which indicates the individualis currently suffering a stroke or the immediate aftereffects. Usingthis approach, historical classes and attributes mirroring the currentclasses and attributes can be created, as illustrated by historicalphysical health class 510 h, historical physical diagnoses class 510.1h, historical basic physical class 520 h, historical height class 520.1h, historical detailed physical class 530 h, and historical hormonelevels class 530.1 h. In an alternate embodiment historical classes andhistorical attributes are not utilized. Rather, time stamping of thediagnoses or event is used. In this approach, an attribute of510.1.4-05FEB03 would indicate that the individual suffered a stroke onFeb. 5, 2003. Alternate classification schemes and attributeclasses/classifications can be used and will be understood by one ofskill in the art. In one embodiment, time stamping of attributes ispreferred in order to permit accurate determination of those attributesor attribute combinations that are associated with an attribute ofinterest (i.e., a query attribute or target attribute) in a causative orpredictive relationship, or alternatively, those attributes or attributecombinations that are associated with an attribute of interest in aconsequential or symptomatic relationship. In one embodiment, onlyattributes bearing a time stamp that predates the time stamp of theattribute of interest are processed by the methods. In anotherembodiment, only attributes bearing a time stamp that postdates the timestamp of the attribute of interest are processed by the methods. Inanother embodiment, both attributes that predate and attributes thatpostdate an attribute of interest are processed by the methods.

As further shown in FIG. 5, physical prognoses subclass 510.2 cancontain attributes related to clinical forecasting of the course andoutcome of disease and chances for recovery. Basic physical class 520can include the attributes age 520.1, sex 520.2, height 520.3, weight520.4, and ethnicity 520.5, whose values provide basic physicalinformation about the individual. Hormone levels 530.1 andstrength/endurance 530.4 are examples of attribute subclasses withindetailed physical class 530. Hormone levels 530.1 can include attributesfor testosterone level, estrogen level, progesterone level, thyroidhormone level, insulin level, pituitary hormone level, and growthhormone level, for example. Strength/endurance 530.4 can includeattributes for various weight lifting capabilities, stamina, runningdistance and times, and heart rates under various types of physicalstress, for example. Blood sugar level 530.2, blood pressure 530.3 andbody mass index 530.5 are examples of attributes whose values providedetailed physical information about the individual. Historical physicalhealth class 510 h, historical basic physical class 520 h and historicaldetailed physical class 530 h are examples of historical attributeclasses. Historical physical health class 510 h can include historicalattribute subclasses such as historical physical diagnoses class 510.hwhich would include attributes for past physical diagnoses of variousdiseases and physical health conditions which may or may not berepresentative of the individual's current health state. Historicalbasic physical class 520 h can include attributes such as historicalheight class 520.1 h which can contain heights measured at particularages. Historical detailed physical class 530 h can include attributesand attribute classes such as the historical hormone levels class 530.1h which would include attributes for various hormone levels measured atvarious time points in the past.

In one embodiment, the classes and indexing illustrated in FIG. 5 anddescribed above can be matched to health insurance information such ashealth insurance codes, such that information collected by health careprofessionals (such as clinician 820 of FIG. 8, which can be aphysician, nurse, nurse practitioner or other health care professional)can be directly incorporated as attribute data. In this embodiment, theheath insurance database can directly form part of the attributedatabase, such as one which can be constructed using the classes of FIG.5.

FIG. 6 illustrates classes of situational attributes as defined bysituational attributes metaclass 600, which in one embodiment caninclude medical class 610, exposures class 620, and financial class 630,for example. In one embodiment, medical class 610 can include treatmentssubclass 610.1 and medications subclass 610.2; exposures class 620 caninclude environmental exposures subclass 620.1, occupational exposuressubclass 620.2 and self-produced exposures 620.3; and financial class630 can include assets subclass 630.1, debt subclass 630.2 and creditreport subclass 630.3. Historical medical class 610 h can includehistorical treatments subclass 610.1 h, historical medications subclass610.2 h, historical hospitalizations subclass 610.3 h and historicalsurgeries subclass 610.4 h. Other historical classes included within thesituational attributes metaclass 600 can be historical exposuressubclass 620 h, historical financial subclass 630 h, historical incomehistory subclass 640 h, historical employment history subclass 650 h,historical marriage/partnerships subclass 660 h, and historicaleducation subclass 670 h.

In one embodiment, commercial databases such as credit databases,databases containing purchase information (e.g. frequent shopperinformation) can be used as either the basis for extracting attributesfor the classes such as those in financial subclass 630 and historicalfinancial subclass 630 h, or for direct mapping of the information inthose databases to situational attributes. Similarly, accountinginformation such as that maintained by the consumer 810 of FIG. 8, or arepresentative of the consumer (e.g. the consumer's accountant) can alsobe incorporated, transformed, or mapped into the classes of attributesshown in FIG. 6.

Measurement of financial attributes such as those illustrated anddescribed with respect to FIG. 6 allows financial attributes such asassets, debt, credit rating, income and historical income to be utilizedin the methods, systems, software and databases described herein. Insome instances, such financial attributes can be important with respectto a query attribute. Similarly, other situational attributes such asthe number of marriages/partnerships, length of marriages/partnership,number jobs held, income history, can be important attributes and willbe found to be related to certain query attributes. In one embodiment asignificant number of attributes described in FIG. 6 are extracted frompublic or private databases, either directly or through manipulation,interpolation, or calculations based on the data in those databases.

FIG. 7 illustrates classes of behavioral attributes as defined bybehavioral attributes metaclass 700, which in one embodiment can includemental health class 710, habits class 720, time usage class 730,mood/emotional state class 740, and intelligence quotient class 750, forexample. In one embodiment, mental health class 710 can includemental/behavioral diagnoses subclass 710.1 and mental/behavioralprognoses subclass 710.2; habits class 720 can include diet subclass720.1, exercise subclass 720.2, alcohol consumption subclass 720.3,substances usage subclass 720.4, and sexual activity subclass 720.5; andtime usage class 730 can include work subclass 730.1, commute subclass730.2, television subclass 730.3, exercise subclass 730.4 and sleepsubclass 730.5. Behavioral attributes metaclass 700 can also includehistorical classes such as historical mental health class 710 h,historical habits 720 h, and historical time usage class 730 h.

As discussed with respect to FIGS. 5 and 6, in one embodiment, externaldatabases such as health care provider databases, purchase records andcredit histories, and time tracking systems can be used to supply thedata which constitutes the attributes of FIG. 7. Also with respect toFIG. 7, classification systems such as those used by mental healthprofessionals such as classifications found in the DSM-IV can be useddirectly, such that the attributes of mental health class 710 andhistorical prior mental health class 710 h have a direct correspondenceto the DSM-IV. The classes and objects of the present invention, asdescribed with respect to FIGS. 5, 6 and 7, can be implemented using anumber of database architectures including, but not limited to flatfiles, relational databases and object oriented databases.

Unified Modeling Language (“UML”) can be used to model and/or describemethods and systems and provide the basis for better understanding theirfunctionality and internal operation as well as describing interfaceswith external components, systems and people using standardizednotation. When used herein, UML diagrams including, but not limited to,use case diagrams, class diagrams and activity diagrams, are meant toserve as an aid in describing the embodiments of the present inventionbut do not constrain implementation thereof to any particular hardwareor software embodiments. Unless otherwise noted, the notation used withrespect to the UML diagrams contained herein is consistent with the UML2.0 specification or variants thereof and is understood by those skilledin the art.

FIG. 8 illustrates a use case diagram for an attribute determinationsystem 800 which, in one embodiment, allows for the determination ofattributes which are statistically relevant or related to a queryattribute. Attribute determination system 800 allows for a consumer 810,clinician 820, and genetic database administrator 830 to interact,although the multiple roles may be filled by a single individual, toinput attributes and query the system regarding which attributes arerelevant to the specified query attribute. In a contribute geneticsample use case 840 a consumer 810 contributes a genetic sample.

In one embodiment this involves the contribution by consumer 810 of aswab of the inside of the cheek, a blood sample, or contribution ofother biological specimen associated with consumer 810 from whichgenetic and epigenetic data can be obtained. In one embodiment, geneticdatabase administrator 830 causes the genetic sample to be analyzedthrough a determine genetic and epigenetic attributes use case 850.Consumer 810 or clinician 820 may collect physical attributes through adescribe physical attributes use case 842. Similarly, behavioral,situational, and historical attributes are collected from consumer 810or clinician 820 via describe behavioral attributes use case 844,describe situational attributes use case 846, and describe historicalattributes use case 848, respectively. Clinician 820 or consumer 810 canthen enter a query attribute through receive query attribute use case852. Attribute determination system 800 then, based on attributes oflarge query-attribute-positive and query-attribute-negative populations,determines which attributes and combinations of attributes, extendingacross the pangenetic (genetic/epigenetic), physical, behavioral,situational, and historical attribute categories, are statisticallyrelated to the query attribute. As previously discussed, and withrespect to FIG. 1 and FIGS. 4-6, historical attributes can, in certainembodiments, be accounted for through the other categories ofattributes. In this embodiment, describe historical attributes use case848 is effectively accomplished through determine genetic and epigeneticattributes use case 850, describe physical attributes use case 842,describe behavioral attributes use case 844, and describe situationalattributes use case 846.

With respect to the aforementioned method of collection, inaccuraciescan occur, sometimes due to outright misrepresentations of theindividual's habits. For example, it is not uncommon for patients toself-report alcohol consumption levels which are significantly belowactual levels. This can occur even when a clinician/physician isinvolved, as the patient reports consumption levels to theclinician/physician that are significantly below their actualconsumption levels. Similarly, it is not uncommon for an individual toover-report the amount of exercise they get.

In one embodiment, disparate sources of data including consumption dataas derived from purchase records, data from blood and urine tests, andother observed characteristics are used to derive attributes such asthose shown in FIGS. 5-7. By analyzing sets of disparate data,corrections to self-reported data can be made to produce more accuratedeterminations of relevant attributes. In one embodiment, heuristicrules are used to generate attribute data based on measured, rather thanself-reported attributes. Heuristic rules are defined as rules whichrelate measurable (or accurately measurable) attributes to lessmeasurable or less reliable attributes such as those from self-reporteddata. For example, an individual's recorded purchases includingcigarette purchases can be combined with urine analysis or blood testresults which measure nicotine levels or another tobacco relatedparameter and heuristic rules can be applied to estimate cigaretteconsumption level. As such, one or more heuristic rules, typically basedon research which statistically links a variety of parameters, can beapplied by data conversion/formatting engine 220 to the datarepresenting the number of packs of cigarettes purchased by anindividual or household, results of urine or blood tests, and otherstudied attributes, to derive an estimate of the extent to which theindividual smokes.

In one embodiment, the heuristic rules take into account attributes suchas household size and self-reported data to assist in the derivation ofthe desired attribute. For example, if purchase data is used in aheuristic rule, household size and even the number of self-reportedsmokers in the household, can be used to help determine actual levels ofconsumption of tobacco by the individual. In one embodiment, householdmembers are tracked individually, and the heuristic rules provide forthe ability to approximately assign consumption levels to differentpeople in the household. Details such as individual brand usages orpreferences may be used to help assign consumptions within thehousehold. As such, in one embodiment the heuristic rules can be appliedby data conversion/formatting engine 220 to a number of disparate piecesof data to assist in extracting one or more attributes.

Physical, behavioral, situational and historical attribute data may bestored or processed in a manner that allows retention of maximumresolution and accuracy of the data while also allowing flexiblecomparison of the data so that important shared similarities betweenindividuals are not overlooked. This can be important when processingnarrow and extreme attribute values, or when using smaller populationsof individuals where the reduced number of individuals makes theoccurrence of identical matches of attributes rare. In these and othercircumstances, flexible treatment and comparison of attributes canreveal predisposing attributes that are related to or legitimatelyderive from the original attribute values but have broader scope, lowerresolution, and extended or compounded values compared to the originalattributes. In one embodiment, attributes and attribute values can bequalitative (categorical) or quantitative (numerical). In anotherembodiment, attributes and attribute values can be discrete orcontinuous numerical values.

There are several ways flexible treatment and comparison of attributescan be accomplished. As shown in FIG. 2, one approach is to incorporatedata conversion/formatting engine 220 which is able to create expanded1st dataset 202 from 1st dataset 200. In one embodiment, 1st dataset 200can comprise one or more primary attributes, or original attributeprofiles containing primary attributes, and expanded 1st dataset 202 cancomprise one or more secondary attributes, or expanded attributeprofiles containing secondary attributes. A second approach is toincorporate functions into attribute comparison engine 222 that allow itto expand the original attribute data into additional values or rangesduring the comparison process. This provides the functional equivalentof reformatting the original dataset without having to create and storethe entire set of expanded attribute values.

In one embodiment, original attributes (primary attributes) can beexpanded into one or more sets containing derived attributes (secondaryattributes) having values, levels or degrees that are above, below,surrounding or including that of the original attributes. In oneembodiment, original attributes can be used to derive attributes thatare broader or narrower in scope than the original attributes. In oneembodiment, two or more original attributes can be used in a computation(i.e., compounded) to derive one or more attributes that are related tothe original attributes. As shown in FIG. 9A, a historical situationalattribute indicating a time span of smoking, from age 25-27, and ahistorical behavioral attribute indicating a smoking habit, 10 packs perweek, may be compounded to form a single value for the historicalsituational attribute of total smoking exposure to date, 1560 packs, asshown in FIG. 9B, by simply multiplying 156 weeks by 10 packs/week.Similar calculations enable the derivation of historical situationalattributes such as total nicotine and total cigarette tar exposure basedon known levels nicotine and tar in the specific brand smoked, Marlboroas indicated by the cigarette brand attribute, multiplied by the totalsmoking exposure to date. In another example, a continuous numericalattribute, {time=5.213 seconds}, can be expanded to derive the discretenumerical attribute, {time=5 seconds}.

Attribute expansion of a discrete numerical attribute, such as age, canbe exemplified in one embodiment using a population comprised of fourindividuals ages 80, 66, 30 and 15. In this example, Alzheimer's diseaseis the query attribute, and both the 80 year old and the 66 year oldindividual have Alzheimer's disease, as indicated by an attribute for apositive Alzheimer's diagnosis in their attribute profiles. Therefore,for this small population, the 80 and 66 year old individuals constitutethe query-attribute-positive group (the group associated with the queryattribute). If a method of discovering attribute associations isexecuted, none of the attribute combinations identified as beingstatistically associated with the query attribute will include age,since the numerical age attributes 80 and 66 are not identical. However,it is already known from empirical scientific research that Alzheimer'sdisease is an age-associated disease, with prevalence of the diseasebeing much higher in the elderly. By using the original (primary) ageattributes to derive new (secondary) age attributes, a method ofdiscovering attribute associations can appropriately identify attributecombinations that contain age as a predisposing attribute forAlzheimer's disease based on the query-attribute-positive group of thispopulation. To accomplish this, a procedure of attribute expansionderives lower resolution secondary age attributes from the primary ageattributes and consequently expands the attribute profiles of theindividuals in this population. This can be achieved by eithercategorical expansion or numerical expansion.

In one embodiment of a categorical attribute expansion, primarynumerical age attributes are used to derive secondary categoricalattributes selected from the following list: infant (ages 0-1), toddler(ages 1-3), child (ages 4-8), preadolescent (ages 9-12), adolescent(ages 13-19), young adult (ages 20-34), mid adult (ages 35-49), lateadult (ages 50-64), and senior (ages 65 and up). This particularattribute expansion will derive the attribute ‘senior’ for the 80 yearold individual, ‘senior’ for the 66 year old, ‘young adult’ for the 30year old, and ‘adolescent’ for the 15 year old. These derived attributescan be added to the respective attribute profiles of these individualsto create an expanded attribute profile for each individual. As aconsequence of this attribute expansion procedure, the 80 and 66 yearold individuals will both have expanded attribute profiles containing anidentical age attribute of ‘senior’, which will be then be identified inattribute combinations that are statistically associated with the queryattribute of Alzheimer's disease, based on a higher frequency ofoccurrence of this attribute in the query-attribute-positive group forthis example.

As an alternative to the above categorical expansion, a numericalattribute expansion can be performed in which numerical age is used toderive a set of secondary numerical attributes comprising a sequence ofinequality statements containing progressively larger numerical valuesthan the actual age and a set of secondary attributes comprising asequence of inequality statements containing progressively smallerquantitative values than the actual age. For example, attributeexpansion can produce the following two sets of secondary age attributesfor the 80 year old: {110>age, 109>age . . . , 82>age, 81>age} and{age>79, age>78 . . . , age>68, age>67, age>66, age>65, age>64 . . . ,age>1, age>0}. And attribute expansion can produce the following twosets of secondary age attributes for the 66 year old: {110>age, 109>age. . . , 82>age, 81>age, 80>age, 79>age, 78>age . . . , 68>age, 67>age}and {age>65, age>64 . . . , age>1, age>0}.

Identical matches of age attributes found in the largest attributecombination associated with Alzheimer's disease, based on the 80 and 66year old individuals that have Alzheimer's in this sample population,would contain both of the following sets of age attributes: {110>age,109>age . . . , 82>age, 81>age} and {age>65, age>64 . . . , age>1,age>0}. This result indicates that being less than 81 years of age butgreater than 65 years of age (i.e., having an age in the range:81>age>65) is a predisposing attribute for having Alzheimer's disease inthis population. This particular method of attribute expansion of ageinto a numerical sequence of inequality statements provides identicalmatches between at least some of the age attributes between individuals,and provides an intermediate level of resolution between actual age andthe broader categorical age attribute of ‘senior’ derived in the firstexample above.

Expansion of age attributes can be also be used for instances in whichage is used to designate a point in life at which a specific activity orbehavior occurred. For example, FIG. 9 demonstrates an example in whichthe actual ages of exposure to smoking cigarettes, ages 25-27, areexpanded into a low resolution categorical age attribute of ‘adult’, abroader numerical age range of ‘21-30’, and a set of age attributescomprising a sequence of progressively larger numerical inequalitystatements for age of the individual, {age>24, age>23 . . . , age>2,age>1}.

Attribute expansion can also be used to reduce the amount of geneticinformation to be processed by the methods of the present invention,essentially 3 billion nucleotides of information per individual andnumerous combinations comprised thereof. For example, attributeexpansion can be used to derive a set of lower resolution geneticattributes (e.g., categorical genetic attributes such as names) that canbe used instead of the whole genomic sequence in the methods.Categorical genetic attributes can be assigned based on only one or afew specific nucleotide attributes out of hundreds or thousands in asequence segment (e.g., a gene, or a DNA or RNA sequence read). However,using only lower resolution categorical genetic attributes may cause thesame inherent limitations of sensitivity as using only SNPs and genomicmarkers, which represent only a portion of the full genomic sequencecontent. So, while categorical genetic attributes can be used to greatlydecrease processing times required for execution of the methods, theyextract a cost in terms of loss of information when used in place of thefull high resolution genomic sequence, and the consequence of this canbe the failure to identify certain predisposing genetic variationsduring execution of the methods. In one embodiment, this can show upstatistically in the form of attribute combinations having lowerstrengths of association with query attributes and/or an inability toidentify any attribute combination having an absolute risk of 1.0 forassociation with a query attribute. So the use of descriptive geneticattributes would be most suitable, and accuracy and sensitivity themethods increased, once the vast majority of influential geneticvariations in the genome (both in gene encoding regions and non-codingregions) have been identified and can be incorporated into rules forassigning categorical genetic attributes.

Instead of being appended to the whole genome sequence attribute profileof an individual, categorical genetic attributes can be used to create aseparate genetic attribute profile for the individual that comprisesthousands of genetic descriptors, rather than billions of nucleotidedescriptors. As an example, 19 different nucleotide mutations have beenidentified in the Cystic Fibrosis Conductance Regulator Gene, each ofwhich can disrupt function of the gene's encoded protein productresulting in clinical diagnosis of cystic fibrosis disease. Since thisis the major known disease associated with this gene, the presence ofany of the 19 mutations can be the basis for deriving a single lowerresolution attribute of ‘CFCR gene with cystic fibrosis mutation’ with astatus value of {1=Yes} to represent possession of the genomic sequenceof one of the diseased variations of this gene, with the remainingsequence of the gene ignored. For individuals that do not possess any ofthe 19 mutations in their copies of the gene, the attribute ‘CFCR genewith cystic fibrosis mutation’ and a status value {0=No} can be derived.This approach not only reduces the amount of genetic information thatneeds to be processed, it allows for creation of an identical geneticattribute associated with 19 different individuals, each possessing oneof 19 different nucleotide mutations in the Cystic Fibrosis ConductanceRegulator Gene, but all having the same gene mutated and sharing thesame disease of cystic fibrosis. This allows for identification ofidentical genetic attribute within their attribute profiles with respectto defect of the CFCR gene without regard for which particularnucleotide mutation is responsible for the defect. This type ofattribute expansion can be performed for any genetic sequence, not justgene encoding sequences, and need not be related to disease phenotypes.Further, the genetic attribute descriptors can be names or numericcodes, for example. In one embodiment, a single categorical geneticattribute descriptor can be used to represent a collection of nucleotidevariations occurring simultaneously across multiple locations of agenetic sequence or genome.

Similar to expansion of genetic attributes, attribute expansion can beperformed with epigenetic attributes. For example, multiple DNAmethylation modifications are known to occur simultaneously at differentnucleotide positions within DNA segments and can act in a cooperativemanner to effect regulation of expression of one gene, or even acollection of genes located at a chromosomal locus. Based on informationwhich indicates that several different patterns of epigenetic DNAmethylation, termed epigenetic polymorphisms, can produce the samephenotypic effect, a single categorical epigenetic attribute descriptorcan be derived as a descriptor for that group of epigenetic DNAmethylation patterns, thereby ensuring the opportunity for an epigeneticattribute match between individuals sharing predisposition to the sameoutcome but having a different epigenetic polymorphism that producesthat outcome. For example, it has been suggested by researchers thatseveral different patterns of epigenetic modification of the HTR2Aserotonin gene locus are capable of predisposing an individual toschizophrenia. For individuals associated with one of these particularschizophrenia-predisposing epigenetic patterns, the same categoricalepigenetic attribute of ‘HTR2A epigenetic schizophrenia pattern’ with astatus value of {1=yes} can be derived. For an individual who isnegative for all known schizophrenia-predisposing epigenetic patterns inthe HTR2A gene, the categorical epigenetic attribute of ‘HTR2Aepigenetic schizophrenia pattern’ with a status value {0=no} can bederived to indicate that the individual does not possess any of theepigenetic modifications of the HTR2A serotonin gene locus that areassociated with predisposition to schizophrenia.

In one embodiment, the original attribute value is retained and theexpanded attribute values provided in addition to allow the opportunityto detect similarities at both the maximal resolution level provided bythe original attribute value and the lower level of resolution and/orbroader coverage provided by the expanded attribute values or attributevalue range. In one embodiment, attribute values are determined fromdetailed questionnaires which are completed by the consumer/patientdirectly or with the assistance of clinician 820. Based on thesequestionnaires, attribute values such as those shown in FIGS. 9A and 9Bcan be derived. In one or more embodiments, when tabulating, storing,transmitting and reporting results of methods of the present invention,wherein the results include both narrow attributes and broad attributesthat encompass those narrow attributes, the broader attributes may beincluded and the narrow attributes eliminated, filtered or masked inorder to reduce the complexity and lengthiness of the final results.

Attribute expansion can be used in a variety of embodiments in whichstatistical associations between attribute combinations and one or morequery attributes are determined. As such, attribute expansion can beperformed to create expanded attribute profiles that are more stronglyassociated with a query attribute than the attribute profiles from whichthey were derived. As explained previously, attribute expansion canaccomplish this by introducing predisposing attributes that were missingor introducing attributes of the correct resolution for maximizingattribute identities between attribute profiles of a group ofquery-attribute-positive individuals. In effect, expansion of attributeprofiles can reveal predisposing attributes that were previously maskedfrom detection and increase the ability of a method that uses theexpanded attribute profiles to predict an individual's risk ofassociation with a query attribute with greater accuracy and certaintyas reflected by absolute risk results that approach either 1.0(certainty of association) or 0.0 (certainty of no association) and havehigher statistical significance. To avoid introducing bias error intomethods of the present invention, expansion of attribute profiles shouldbe performed according to a set of rules, which can be predetermined, sothat identical types of attributes are expanded in the attributeprofiles of all individuals processed by the methods. For example, if amethod processes the attribute profiles of a group of query-positiveindividuals and a group of query-attribute-negative individuals, and thequery-attribute-positive individuals have had their primary ageattributes expanded into secondary categorical age attributes which havebeen added to their attribute profiles, then attribute expansion of theprimary age attributes of the query-attribute-negative individualsshould also be performed according to the same rules used for thequery-attribute-positive individuals before processing any of theattribute profiles by the method. Ensuring uniform application ofattribute expansion across a collection of attribute profiles willminimize introducing considerable bias into those methods that useexpanded attribute profiles or data derived from them.

Consistent with the various embodiments of the present inventiondescribed herein, computer based systems (which can comprise a pluralityof subsystems), datasets, databases and software can be implemented formethods of generating and using secondary attributes and expandedattribute profiles.

In one embodiment, a computer based method for compiling attributecombinations using expanded attribute combinations is provided. A queryattribute is received, and a set of expanded attribute profilesassociated with a group of query-attribute-positive individuals and aset of expanded attribute profiles associated with a group ofquery-attribute-negative individuals are accessed, both sets of expandedattribute profiles comprising a set of primary attributes and a set ofsecondary attributes, wherein the set of secondary attributes is derivedfrom the set of primary attributes and has lower resolution than the setof primary attributes. Attribute combinations having a higher frequencyof occurrence in the set of expanded attribute profiles associated withthe group of query-attribute-positive individuals than in the set ofexpanded attribute profiles associated with the group ofquery-attribute-negative individuals are identified. The identifiedattribute combinations are stored to create a compilation of attributecombinations that co-occur with the query attribute (i.e., an attributecombination database).

In one embodiment, a computer based method for expanding attributeprofiles to increase the strength of association between a queryattribute and a set of attribute profiles associated withquery-attribute-positive individuals is provided. A query attribute isreceived, and a set of attribute profiles associated with a group ofquery-attribute-positive individuals and a set of attribute profilesassociated with a group of query-attribute-negative individuals areaccessed. A first statistical result indicating strength of associationof the query attribute with an attribute combination having a higherfrequency of occurrence in the set of attribute profiles associated withthe group of query-attribute-positive individuals than in the set ofattribute profiles associated with the group of query-attribute-negativeindividuals is determined. One or more attributes in the set ofattribute profiles associated with the group of query-attribute-positiveindividuals and one or more attributes in the set of attribute profilesassociated with the query-attribute-negative individuals are expanded tocreate a set of expanded attribute profiles associated with the group ofquery-attribute-positive individuals and a set of expanded attributeprofiles associated with the group of query-attribute-negativeindividuals. A second statistical result indicating strength ofassociation of the query attribute with an attribute combination havinga higher frequency of occurrence in the set of expanded attributeprofiles associated with the group of query-attribute-positiveindividuals than in the set of expanded attribute profiles associatedwith the group of query-attribute-negative individuals is determined. Ifthe second statistical result is higher than the first statisticalresult, the expanded attribute profiles associated with the group ofquery-attribute-positive individuals and the expanded attribute profilesassociated with the group of query-attribute-negative individuals arestored.

In one embodiment, a computer based method for determining attributeassociations using an expanded attribute profile is provided. A queryattribute is received, and one or more primary attributes in anattribute profile associated with a query-attribute-positive individualare accessed. One or more secondary attributes are the derived from theprimary attributes such that the secondary attributes are lowerresolution attributes than the primary attributes. The secondaryattributes are stored in association with the attribute profile tocreate an expanded attribute profile. Attribute combinations that areassociated with the query attribute are determined by identifyingattribute combinations from the expanded attribute profile that havehigher frequencies of occurrence in a set of attribute profilesassociated with a group of query-attribute-positive individuals than ina set of attribute profiles associated with a group ofquery-attribute-negative individuals.

In one embodiment, a computer based method for determining attributeassociations using an expanded attribute profile is provided in whichone or more primary attributes in an attribute profile are accessed. Oneor more secondary attributes are generated from the primary attributessuch that the secondary attributes have lower resolution than theprimary attributes. The secondary attributes are stored in associationwith the attribute profile to create an expanded attribute profile. Thestrength of association between the expanded attribute profile and aquery attribute is determined by comparing the expanded attributeprofile to a set of attribute combinations that are statisticallyassociated with the query attribute.

The methods, systems, software and databases described herein are ableto achieve determination of complex combinations of predisposingattributes not only as a consequence of the resolution and breadth ofdata used, but also as a consequence of the process methodology used fordiscovery of predisposing attributes. An attribute may have no effect onexpression of another attribute unless it occurs in the proper context,the proper context being co-occurrence with one or more additionalpredisposing attributes. In combination with one or more additionalattributes of the right type and degree, an attribute may be asignificant contributor to predisposition of the organism for developingthe attribute of interest. This contribution is likely to remainundetected if attributes are evaluated individually. As an example,complex diseases require a specific combination of multiple attributesto promote expression of the disease. The required disease-predisposingattribute combinations will occur in a significant percentage of thosethat have or develop the disease and will occur at a lower frequency ina group of unaffected individuals.

FIG. 10 illustrates an example of the difference in frequencies ofoccurrence of attributes when considered in combination as opposed toindividually. In the example illustrated, there are two groups ofindividuals referred to based on their status of association with aquery attribute (a specific attribute of interest that can be submittedin a query). One group does not possess (is not associated with) thequery attribute, the query-attribute-negative group, and the other doespossess (is associated with) the query attribute, thequery-attribute-positive group. In one embodiment, the query attributeof interest is a particular disease or trait. The two groups areanalyzed for the occurrence of two attributes, A and X, which arecandidates for causing predisposition to the disease. When frequenciesof occurrence are computed individually for A and for X, the observedfrequencies are identical (50%) for both groups. When the frequency ofoccurrence is computed for the combination of A with X for individualsof each group, the frequency of occurrence is dramatically higher in thepositive group compared to the negative group (50% versus 0%).Therefore, while both A and X are significant contributors topredisposition in this theoretical example, their association withexpression of the disease in individuals can only be detected bydetermining the frequency of co-occurrence of A with X in eachindividual.

FIG. 11 illustrates another example of the difference in frequencies ofoccurrence of attributes when considered in combination as opposed toindividually. In this example there are again two groups of individualsthat are positive or negative for an attribute of interest submitted ina query, which could again be a particular disease or trait of interest.Three genes are under consideration as candidates for causingpredisposition to the query attribute. Each of the three genes has threepossible alleles (each labeled A, B, or C for each gene). This examplenot only illustrates the requirement for attributes occurring incombination to cause predisposition, but also the phenomenon that therecan be multiple different combinations of attributes that produce thesame outcome. In the example, a combination of either all A, all B, orall C alleles for the genes can result in predisposition to the queryattribute. The query-attribute-positive group is evenly divided amongthese three attribute combinations, each having a frequency ofoccurrence of 33%. The same three combinations occur with 0% frequencyin the query-attribute-negative group. However, if the attributes areevaluated individually, the frequency of occurrence of each allele ofeach gene is an identical 33% in both groups, which would appear toindicate no contribution to predisposition by any of the alleles in onegroups versus the other. As can be seen from FIG. 11, this is not thecase, since every gene allele considered in this example does contributeto predisposition toward the query attribute when occurring in aparticular combination of alleles, specifically a combination of all A,all B, or all C. This demonstrates that a method of attributepredisposition determination needs to be able to detect attributes thatexpress their predisposing effect only when occurring in particularcombinations. It also demonstrates that the method should be able todetect multiple different combinations of attributes that may all causepredisposition to the same query attribute.

Although the previous two figures present frequencies of occurrence aspercentages, for the methods of the present invention the frequencies ofoccurrence of attribute combinations are can be stored as ratios forboth the query-attribute-positive individuals and thequery-attribute-negative individuals. Referring to FIG. 12A and FIG.12B, the frequency of occurrence for the query-attribute-positive groupis the ratio of the number of individuals of that group having theattribute combination (the exposed query-attribute-positive individualsdesignated ‘a’) to the total number of individuals in that group (‘a’plus ‘c’). The number of individuals in the query-attribute-positivegroup that do not possess the attribute combination (the unexposedquery-attribute-positive individuals designated ‘c’) can either betallied and stored during comparison of attribute combinations, orcomputed afterward from the stored frequency as the total number ofindividuals in the group minus the number of exposed individuals in thatgroup (i.e., (a+c)−a=c). For the same attribute combination, thefrequency of occurrence for the query-attribute-negative group is theratio of the number of individuals of that group having the attributecombination (the exposed query-attribute-negative individuals designated‘b’) to the total number of individuals in that group (‘b’ plus ‘d’).The number of individuals in the query-attribute-negative group that donot possess the attribute combination (the unexposedquery-attribute-negative individuals designated ‘d’) can either betallied and stored during comparison of attribute combinations or can becomputed afterward from the stored frequency as the total number ofindividuals in the group minus the number of exposed individuals in thatgroup (i.e., (b+d)−b=d).

The frequencies of occurrence of an attribute or attribute combination,when compared for two or more groups of individuals with respect to aquery attribute, are statistical results that can indicate strength ofassociation of the attribute combination with a query attribute.Frequencies of occurrence can also be utilized by statisticalcomputation engine 224 to compute additional statistical results forstrength of association of the attribute combinations with the queryattribute. The statistical measures used may include, but are notlimited to, prevalence, incidence, probability, absolute risk, relativerisk, attributable risk, excess risk, odds (a.k.a. likelihood), and oddsratio (a.k.a. likelihood ratio). Absolute risk (a.k.a. probability),relative risk, odds, and odds ratio are the preferred statisticalcomputations for the present invention. Among these, absolute risk andrelative risk are the more preferable statistical computations becausetheir values can still be calculated for an attribute combination ininstances where the frequency of occurrence of the attribute combinationin the query-attribute-negative group is zero. Odds and odds ratio areundefined in instances where the frequency of occurrence of theattribute combination in the query-attribute-negative group is zero,because in that situation their computation requires division by zerowhich is mathematically undefined. One embodiment of the presentinvention, when supplied with ample data, is expected to routinely yieldfrequencies of occurrence of zero in query-attribute-negative groupsbecause of its ability to discover large predisposing attributecombinations that are exclusively associated with the query attribute.

FIG. 12B illustrates formulas for the statistical measures that can beused to compute statistical results. In one embodiment, absolute risk iscomputed as the probability that an individual has or will develop thequery attribute given exposure to an attribute combination. In oneembodiment, relative risk is computed as the ratio of the probabilitythat an exposed individual has or will develop the query attribute tothe probability that an unexposed individual has or will develop thequery attribute. In one embodiment, odds is computed as the ratio of theprobability that an exposed individual has or will develop the queryattribute (absolute risk of the exposed query-attribute-positiveindividuals) to the probability that an exposed individual does not haveand will not develop the query attribute (absolute risk of the exposedquery-attribute-negative individuals). In one embodiment, the odds ratiois computed as the ratio of the odds that an exposed individual has orwill develop the query attribute to the odds that an unexposedindividual has or will develop the query attribute.

In one embodiment, results for absolute risk and relative risk can beinterpreted as follows with respect to an attribute combinationpredicting association with a query attribute: 1) if absolute risk=1.0,and relative risk=undefined, then the attribute combination issufficient and necessary to cause association with the query attribute,2) if absolute risk=1.0, and relative risk is not equal to undefined,then the attribute combination is sufficient but not necessary to causeassociation with the query attribute, 3) if absolute risk<1.0, andrelative risk is not equal to undefined, then the attribute combinationis neither sufficient nor necessary to cause association with the queryattribute, and 4) if absolute risk<1.0, and relative risk=undefined,then the attribute combination is not sufficient but is necessary tocause association with the query attribute. In an alternate embodiment,relative risk=undefined can be interpreted to mean that there are two ormore attribute combinations, rather than just one attribute combination,that can cause association with the query attribute. In one embodiment,an absolute risk<1.0 can be interpreted to mean one or more of thefollowing: 1) the association status of one or more attributes, asprovided to the methods, is inaccurate or missing (null), 2) not enoughattributes have been collected, provided to or processed by the methods,or 3) the resolution afforded by the attributes that have been providedis too narrow or too broad. These interpretations can be used toincrease accuracy and utility of the methods for use in manyapplications including but not limited to attribute combinationdiscovery, attribute prediction, predisposition prediction,predisposition modification and destiny modification.

The statistical results obtained from computing the statistical measurescan be subjected to inclusion, elimination, filtering, and evaluationbased on meeting one or more statistical requirements which may bepredetermined, predesignated, preselected or alternatively, computed denovo based on the statistical results. Statistical requirements caninclude but are not limited to numerical thresholds, statistical minimumor maximum values, and statistical significance/confidence values.

One embodiment of the present invention can be used in many types ofstatistical analyses including but not limited to Bayesian analyses(e.g., Bayesian probabilities, Bayesian classifiers, Bayesianclassification tree analyses, Bayesian networks), linear regressionanalyses, non-linear regression analyses, multiple linear regressionanalyses, uniform analyses, Gaussian analyses, hierarchical analyses,recursive partitioning (e.g., classification and regression trees),resampling methods (e.g., bootstrapping, cross-validation, jackknife),Markov methods (e.g., Hidden Markov Models, Regular Markov Models,Markov Blanket algorithms), kernel methods (e.g., Support VectorMachine, Fisher's linear discriminant analysis, principle componentsanalysis, canonical correlation analysis, ridge regression, spectralclustering, matching pursuit, partial least squares), multivariate dataanalyses including cluster analyses, discriminant analyses and factoranalyses, parametric statistical methods (e.g., ANOVA), non-parametricinferential statistical methods (i.e., binomial test, Anderson-Darlingtest, chi-square test, Cochran's Q, Cohen's kappa, Efron-Petrosian Test,Fisher's exact test, Friedman two-way analysis of variance by ranks,Kendall's tau, Kendall's W, Kolmogorov-Smirnov test, Kruskal-Wallisone-way analysis of variance by ranks, Kuiper's test, Mann-Whitney U orWilcoxon rank sum test, McNemar's test, median test, Pitman'spermutation test, Siegel-Tukey test, Spearman's rank correlationcoefficient, Student-Newman-Keuls test, Wald-Wolfowitz runs test,Wilcoxon signed-rank test).

In one embodiment, the methods, databases, software and systems of thepresent invention can be used to produce data for use in and/or resultsfor the above statistical analyses. In another embodiment, the methods,databases, software and systems of the present invention can be used toindependently verify the results produced by the above statisticalanalyses.

In one embodiment a method is provided which accesses a first datasetcontaining attributes associated with a set of query-attribute-positiveindividuals and query-attribute-negative individuals, the attributesbeing pangenetic, physical, behavioral and situational attributesassociated with individuals, and creates a second dataset of attributesassociated with a query-attribute-positive individual but not associatedwith one or more query-attribute-negative individuals. A third datasetcan be created containing attributes of the second dataset that areeither associated with one or more query-attribute-positive individualsor are not present in any of the query-attribute-negative individuals,along with the frequency of occurrence in the query-attribute-positiveindividuals and the frequency of occurrence in thequery-attribute-negative individuals. A statistical computation can beperformed for each attribute combination, based on the frequency ofoccurrence, the statistical computation result indicating the strengthof association, as measured by one or more well known statisticalmeasures, between each attribute combination and the query attribute.The process can be repeated for a number of query attributes, andmultiple query-positive individuals can be studied to create acomputer-stored and machine-accessible compilation of differentattribute combinations that co-occur with the queried attributes. Thecompilation can be ranked and co-occurring attribute combinations nothaving a minimum strength of association with the query attribute can beeliminated from the compilation.

Similarly, a system can be developed which contains a subsystem foraccessing a query attribute, a second subsystem for accessing a set ofdatabases containing pangenetic, physical, behavioral, and situationalattributes associated with a plurality of query-attribute-positive, andquery-attribute-negative individuals, a data processing subsystem foridentifying combinations of pangenetic, physical, behavioral, andsituational attributes associated with query-attribute-positiveindividuals, but not with query-attribute-negative individuals, and acalculating subsystem for determining a set of statistical results thatindicates a strength of association between the combinations ofpangenetic, physical, behavioral, and situational attributes with thequery attribute. The system can also include a communications subsystemfor retrieving at least some of pangenetic, physical, behavioral, andsituational attributes from at least one external database; a rankingsubsystem for ranking the co-occurring attributes according to thestrength of the association of each co-occurring attribute with thequery attribute; and a storage subsystem for storing the set ofstatistical results indicating the strength of association between thecombinations of pangenetic, physical, behavioral, and situationalattributes and the query attribute. The various subsystems can bediscrete components, configurations of electronic circuits within othercircuits, software modules running on computing platforms includingclasses of objects and object code, or individual commands or lines ofcode working in conjunction with one or more Central Processing Units(CPUs). A variety of storage units can be used including but not limitedto electronic, magnetic, electromagnetic, optical, opto-magnetic andelectro-optical storage.

In one application the method and/or system is used in conjunction witha plurality of databases, such as those that would be maintained byhealth-insurance providers, employers, or health-care providers, whichserve to store the aforementioned attributes. In one embodiment thepangenetic (genetic and epigenetic) data is stored separately from theother attribute data and is accessed by the system/method. In anotherembodiment the pangenetic data is stored with the other attribute data.A user, such as a clinician, physician or patient, can input a queryattribute, and that query attribute can form the basis for determinationof the attribute combinations associated with that query attribute. Inone embodiment the associations will have been previously stored and areretrieved and displayed to the user, with the highest ranked (moststrongly associated) combinations appearing first. In an alternateembodiment the calculation is made at the time the query is entered, anda threshold can be used to determine the number of attributecombinations that are to be displayed.

FIG. 13 illustrates a flowchart of one embodiment of a method forcreation of a database of attribute combinations, wherein 1st dataset1322, 2nd dataset 1324, 3rd dataset 1326 and 4th dataset 1328 correspondto 1st dataset 200, 2nd dataset 204, 3rd dataset 206 and 4th dataset 208respectively of the system illustrated in FIG. 2. Expanded 1st dataset202 of FIG. 2 is optional for this embodiment of the method and istherefore not illustrated in the flowchart of FIG. 13. One aspect ofthis method is the comparison of attributes and attribute combinationsof different individuals in order to identify those attributes andattribute combinations that are shared in common between thoseindividuals. Any attribute that is present in the dataset record of anindividual is said to be associated with that individual.

1st dataset 1322 in the flow chart of FIG. 13 represents the initialdataset containing the individuals' attribute dataset records to beprocessed by the method. FIG. 14 illustrates an example of the contentof a 1st dataset representing attribute data for 111 individuals. Eachindividual's association with attributes A-Z is indicated by either anassociation status value of 0 (no, does do not possess the attribute) ora status value of 1 (yes, does possess the attribute). In oneembodiment, this is preferred format for indicating the presence orabsence of association of an attribute with an individual. In analternate embodiment, an individual's attribute profile or datasetrecord contains the complete set of attributes under consideration and a0 or 1 status value for each. In other embodiments, representation ofassociation of an attribute with an individual can be more complex thanthe simple binary value representations of yes or no, or numerical 1 or0. In one embodiment, the presence of attributes themselves, for examplethe actual identity of nucleotides, a brand name, or a trait representedby a verbal descriptor, can be used to represent the identity, degreeand presence of association of the attribute. In one embodiment, theabsence of an attribute is itself an attribute that can be referred toand/or represented as a ‘not-attribute’. In one embodiment, anot-attribute simply refers to an attribute having a status value of 0,and in a further embodiment, the not-attribute is determined to beassociated with an individual or present in an attribute profile (i.e.,dataset, database or record) if the corresponding attribute has a statusvalue of 0 associated with the individual or is present in the attributeprofile as an attribute with a status value of 0, respectively. Inanother embodiment, a not-attribute can be an attribute descriptorhaving a ‘not’ prefix, minus sign, or alternative designation impartingessentially the same meaning. In a further embodiment, not-attributesare treated and processed no differently than other attributes. Incircumstances where data for an attribute or an attribute's associationstatus cannot be obtained for an individual, the attribute or attributestatus may be omitted and represented as a null. Typically, a nullshould not be treated as being equivalent to a value of zero, since anull is not a value. A null represents the absence of a value, such aswhen no attribute or attribute association status is entered into adataset for a particular attribute.

In the example illustrated in FIG. 14, individuals #1-10 and #111possess unique attribute content which is not repeated in otherindividuals of this population. Individuals #11-20 are representative ofindividuals #21-100, so that the data for each of the individuals #11-20is treated as occurring ten times in this population of 111 individuals.In other words, there are nine other individuals within the group ofindividuals #21-100 (not shown in the table) that have A-Z attributevalues identical to those of individual #11. The same is true forindividuals #12, #13, #14, #15, #16, #17, #18, #19 and #20.

As shown in the flowchart of FIG. 13, in one embodiment the methodbegins with access query attribute step 1300 in which query attribute1320, provided either by a user or by automated submission, is accessed.For this example the query attribute is ‘A’. In access data step 1302,the attribute data for individuals as stored in 1st dataset 1322 areaccessed with query attribute 1320 determining classification of theindividuals as either query-attribute-positive individuals (thoseindividuals that possess the query attribute in their 1st datasetrecord) or query-attribute-negative individuals (those individuals thatdo not possess the query attribute in their dataset record). For queryattribute ‘A’, individuals #1-10 are the query-attribute-positiveindividuals, and individuals #11-111 are the query-attribute-negativeindividuals.

In select query-attribute-positive individual_(N) step 1304, individual#1 is selected in this example for comparison of their attributes withthose of other individuals. In store attributes step 1306, thoseattributes of the selected individual #1 that are not associated with aportion (e.g., one or more individuals) of the query-attribute-negativegroup (or alternatively, a randomly selected subgroup ofquery-attribute-negative individuals) are stored in 2nd dataset 1324 aspotential candidate attributes for contributing to predisposition towardthe query attribute. In one embodiment this initial comparison step isused to increase efficiency of the method by eliminating thoseattributes that are associated with all of the query-attribute-negativeindividuals. Because such attributes occur with a frequency of 100% inthe query-attribute-negative group, they cannot occur at a higherfrequency in the query-attribute-positive group and are therefore notcandidates for contributing to predisposition toward the queryattribute. Therefore, this step ensures that only attributes of theindividual that occur with a frequency of less than 100% in thequery-attribute-negative group are stored in the 2nd dataset. This stepis especially useful for handling genetic attributes since the majorityof the approximately three billion nucleotide attributes of the humangenome are identically shared among individuals and may be eliminatedfrom further comparison before advancing to subsequent steps.

As mentioned above, this initial comparison to effectively eliminateattributes that are not potential candidates may be performed against arandomly selected subgroup of query-attribute-negative individuals.Using a small subgroup of individuals for the comparison increases theefficiency and prevents the need to perform a comparison against theentire query-attribute-negative population which may consist ofthousands or even millions of individuals. In one embodiment, such asubgroup preferably has as many as 20, but may even have as few as 10,randomly selected query-attribute-negative individuals.

For the present example, only those attributes having a status value of1 for individual #1 and a status value of 0 for one or morequery-attribute-negative individuals are stored as potential candidateattributes, but in one embodiment those attributes having a status valueof 0 for individual #1 and a status value of 1 for one or morequery-attribute-negative individuals (i.e., attributes I, K, Q and W)can also be stored as candidate attributes, and may be referred to ascandidate not-attributes of individual #1. FIG. 15A illustrates the 2nddataset which results from processing the attributes of individual #1for query attribute ‘A’ in a comparison against individuals #11-111 ofthe query-attribute-negative subgroup. The stored candidate attributesconsist of C, E, F, N, T and Y. FIG. 15B illustrates a tabulation of allpossible combinations of these attributes. In store attributecombinations step 1308, those combinations of attributes of 2nd dataset1324 that are found by comparison to be associated with one or morequery-attribute-positive individuals of 1st dataset 1322 are stored in3rd dataset 1326 along with the corresponding frequencies of occurrencefor both groups determined during the comparison. Although not relevantto this example, there may be instances in which a particular attributecombination is rare enough, or the group sizes small enough, that theselected query-attribute-positive individual is the only individual thatpossesses that particular attribute combination. Under suchcircumstances, no other individual of the query-attribute-positive groupand no individual of the query-attribute-negative group will be found topossess that particular attribute combination. To ensure that theattribute combination is stored as a potential predisposing attributecombination, one embodiment of the method can include a requirement thatany attribute combination not present in any of thequery-attribute-negative individuals be stored in the 3rd dataset alongwith the frequencies of occurrence for both groups. Any attributecombination stored according to this rule necessarily has a frequency ofoccurrence equal to zero for the query-attribute-negative group and afrequency of occurrence having a numerator equal to one for theattribute-positive group.

FIG. 16 illustrates a 3rd dataset containing a representative portion ofthe stored attribute combinations and their frequencies of occurrencefor the data of this example. Each frequency of occurrence is preferablystored as a ratio of the number of individuals of a group that areassociated with the attribute combination in the numerator and the totalnumber of individuals of that group in the denominator.

In store statistical results step 1310, the frequencies of occurrencepreviously stored in 3rd dataset 1326 are used to compute statisticalresults for the attribute combinations which indicate the strength ofassociation of each attribute combination with the query attribute. Asmentioned previously, the statistical computations used may includeprevalence, incidence, absolute risk (a.k.a. probability), attributablerisk, excess risk, relative risk, odds and odds ratio. In oneembodiment, absolute risk, relative risk, odds and odds ratio are thestatistical computations performed (see formulas in FIG. 12B). Computedstatistical results stored with their corresponding attributecombinations are shown in the 3rd dataset illustrated by FIG. 16. Theodds and odds ratio computations for the attribute combinations CEFNTY,CEFNT, CEFNY, CFNTY and CEFN are shown as undefined in this 3rd datasetexample because the frequencies of occurrence of these attributecombinations in the query-attribute-positive group are zero.

For the sake of brevity, only the individual #1 was selected andprocessed in the method, thereby determining only the predisposingattribute combinations of individual #1 and those individuals of thegroup that also happen to possess one or more of those attributecombinations. However, one can proceed to exhaustively determine allpredisposing attribute combinations in the query-attribute-positivegroup and build a complete 3rd dataset for the population with respectto query attribute ‘A’. As shown in the flow chart of FIG. 13, this isachieved by simply including decision step 1312 to provide a choice ofselecting successive individuals from the query-attribute-positive groupand processing their attribute data through successive iteration ofsteps 1300-1310 one individual at a time until all have been processed.The resulting data for each additional individual is simply appendedinto the 3rd dataset during each successive iteration. When selectingand processing multiple individuals, data in the 2nd dataset ispreferably deleted between iterations, or uniquely identified for eachindividual. This will ensure that any data in the 2nd datasetoriginating from a previous iteration is not reconsidered in current andsubsequent iterations of other individuals in the group. Alternatetechniques to prevent reconsideration of the data can be utilized.

In store significantly associated attribute combinations step 1314, 4thdataset 1328 may be created by selecting and storing only thoseattribute combinations and their associated data from the 3rd datasethaving a minimum statistical association with the query attribute. Theminimum statistical association can be a positive, negative or neutralassociation, or combination thereof, as determined by the user or thesystem. This determination can be made based on the statistical resultspreviously stored in 3rd dataset 1326. As an example, the determinationcan be made based on the results computed for relative risk.Statistically, a relative risk of >1.0 indicates a positive associationbetween the attribute combination and the query attribute, while arelative risk of 1.0 indicates no association, and a relative risk of<1.0 indicates a negative association.

FIG. 17 illustrates a 4th dataset consisting of attribute combinationswith a relative risk>1.0, from which the attribute combinations CETY andCE are excluded because they have associated relative risks < or =1.0.FIG. 18 illustrates another example of a 4th dataset that can becreated. In this example, a minimum statistical association requirementof either relative risk>4.0 or absolute risk>0.3 produce this 4thdataset.

It can be left up to the user or made dependent on the particularapplication as to which statistical measure and what degree ofstatistical association is used as the criteria for determininginclusion of attribute combinations in the 4th dataset. In this way, 4thdataset 1328 can be presented in the form of a report which containsonly those attribute combinations determined to be predisposing towardthe query attribute above a selected threshold of significantassociation for the individual or population of individuals.

In many applications it will be desirable to determine predisposingattribute combinations for additional query attributes within the samepopulation of individuals. In one embodiment this is accomplished byrepeating the entire method for each additional query attribute andeither creating new 2nd, 3rd and 4th datasets, or appending the resultsinto the existing datasets with associated identifiers that clearlyindicate what data results correspond to which query attributes. In thisway, a comprehensive database containing datasets of predisposingattribute combinations for many different query attributes may becreated.

In one embodiment of a method for creating an attribute combinationsdatabase, attribute profile records of individuals that have nulls forone or more attribute values are not processed by the method or areeliminated from the 1st dataset before initiating the method. In anotherembodiment, attribute profile records of individuals that have nulls forone or more attribute values are only processed by the method if thoseattribute values that are nulls are deemed inconsequential for theparticular query or application. In another embodiment, a population ofindividuals having one or more individual attribute profile recordscontaining nulls for one or more attribute values are only processed forthose attributes that have values (non-nulls) for every individual ofthat population.

In one embodiment of a method for creating an attribute combinationsdatabase, frequencies of occurrence and statistical results for strengthof association of existing attribute combinations in the attributecombinations dataset are updated based on the attribute profile of anindividual processed by the method. In another embodiment, frequenciesof occurrence and statistical results for strength of association ofexisting attribute combinations in the attribute combinations datasetare not updated based on the attribute profile of an individualprocessed by the method. In another embodiment, the processing of anindividual by the method can require first comparing the individuals'attribute profile to the preexisting attribute combinations dataset todetermine which attribute combinations in the dataset are also presentin the individual's attribute profile, and then in a further embodiment,based on the individual's attribute profile, updating the frequencies ofoccurrence and statistical results for strength of association of thoseattribute combinations in the dataset that are also present in theindividual's attribute profile, without further processing theindividual or their attributes by the method.

The 3rd and 4th datasets created by performing the above methods forcreation of a database of attribute combinations can be used foradditional methods of the invention that enable: 1) identification ofpredisposing attribute combinations toward a key attribute of interest,2) predisposition prediction for an individual toward a key attribute ofinterest, and 3) intelligent individual destiny modification provided aspredisposition predictions resulting from the addition or elimination ofspecific attribute associations.

In one embodiment a method of identifying predisposing attributecombinations is provided which accesses a first dataset containingattribute combinations and statistical computation results that indicatethe potential of each attribute combination to co-occur with a queryattribute, the attributes being pangenetic, physical, behavioral, andsituational attributes. A tabulation can be performed to provide, basedon the statistical computation results, those predisposing attributecombinations that are most likely to co-occur with the query attribute,or a rank-ordering of predisposing attribute combinations of the firstdataset that co-occur with the query attribute.

Similarly, a system can be developed which contains a subsystem foraccessing or receiving a query attribute, a second subsystem foraccessing a dataset containing attribute combinations comprisingpangenetic, physical, behavioral and situational attributes thatco-occur with one or more query attributes, a communications subsystemfor retrieving the attribute combinations from at least one externaldatabase, and a data processing subsystem for tabulating the attributecombinations. The various subsystems can be discrete components,configurations of electronic circuits within other circuits, softwaremodules running on computing platforms including classes of objects andobject code, or individual commands or lines of code working inconjunction with one or more Central Processing Units (CPUs). A varietyof storage units can be used including but not limited to electronic,magnetic, electromagnetic, optical, opto-magnetic and electro-opticalstorage.

In one application the method and/or system is used in conjunction withone or more databases, such as those that would be maintained byhealth-insurance providers, employers, or health-care providers, whichcan serve to store the aforementioned attribute combinations andcorresponding statistical results. In one embodiment the attributecombinations are stored in a separate dataset from the statisticalresults and the correspondence is achieved using identifiers or keyspresent in (shared across) both datasets. In another embodiment theattribute combinations and corresponding statistical results data arestored with other attribute data. A user, such as a clinician, physicianor patient, can input a query attribute, and that query attribute canform the basis for tabulating attribute combinations associated withthat query attribute. In one embodiment the associations have beenpreviously stored and are retrieved and displayed to the user, with thehighest ranked (most strongly associated) combinations appearing first.In an alternate embodiment the tabulation is performed at the time thequery attribute is entered and a threshold used to determine the numberof attribute combinations to be displayed.

FIG. 19 illustrates a flow chart for a method of attributeidentification providing tabulation of attribute combinations that arepredisposing toward an attribute of interest provided in a query. Inreceive query attribute step 1900, query attribute 1920 can be providedas one or more attributes in a query by a user. Alternatively, queryattribute 1920 can be provided by automated submission, as part of a setof one or more stored attributes for example. In access co-occurringattribute combinations step 1902, 1st dataset 1922 is accessed, whereinthis 1st dataset contains attribute combinations that co-occur with thequery attribute and statistical results that indicate the correspondingstrength of association with the query attribute. For this example thequery attribute is ‘A’, and a representative 1st dataset is shown inFIG. 16. In tabulate predisposing attribute combinations step 1904,co-occurring attribute combinations are tabulated, preferably accordingto a rank assigned to each attribute combination based on the strengthof association with the query attribute. Further, attribute combinationscan be included or excluded based on a statistical requirement. Forexample, attribute combinations below the minimum strength ofassociation may be excluded. In one embodiment, a minimum strength ofassociation can be specified by the user in reference to one or morestatistical results computed for the attribute combinations.

As an example, a minimum strength of association requiring relativerisk > or =1.0 may be chosen. Based on this chosen requirement, thetabulated list of attribute combinations shown in FIG. 20 would resultfrom processing the 1st dataset represented in FIG. 16. The attributecombinations are ordered according to rank. In this example, rank valueswere automatically assigned to each attribute combination based on thenumber of attributes in each attribute combination and the magnitude ofthe corresponding absolute risk value. The higher the absolute riskvalue, the lower the numerical rank assigned. For attribute combinationshaving the same absolute risk, those with more total attributes percombination receive a lower numerical rank. This treatment is based ontwo tendencies of larger predisposing attribute combinations. The firstis the general tendency of predisposing attribute combinationscontaining more attributes to possess a higher statistical strength ofassociation with the query attribute. The second is the general tendencyfor elimination of a single attribute from larger combinations ofpredisposing attributes to have less of an effect on strength ofassociation with the query attribute. The resulting tabulated list ofFIG. 20 therefore provides an rank-ordered listing of predisposingattribute combinations toward attribute ‘A’, where the first attributecombination in the listing is ranked as the most predisposing attributecombination identified and the last attribute combination in the listingis ranked as the least predisposing attribute combination of allpredisposing attribute combinations identified for the population ofthis example.

In one embodiment a method for predicting predisposition of anindividual for query attributes of interest is provided which accesses afirst dataset containing attributes associated with an individual and asecond dataset containing attribute combinations and statisticalcomputation results that indicate strength of association of eachattribute combination with a query attribute, the attributes beingpangenetic, physical, behavioral and situational attributes. Acomparison can be performed to determine the largest attributecombination of the second dataset that is also present in the firstdataset and that meets a minimum statistical requirement, the resultbeing stored in a third dataset. The process can be repeated for anumber of query attributes. A tabulation can be performed to provide apredisposition prediction listing indicating the predisposition of theindividual for each of the query attributes. In one embodiment,predisposition can be defined as a statistical result indicatingstrength of association between an attribute or attribute combinationand a query attribute.

Similarly, a system can be developed which contains a subsystem foraccessing or receiving a query attribute, a second subsystem foraccessing a dataset containing attributes of an individual, a thirdsubsystem for accessing attribute combinations of pangenetic, physical,behavioral, and situational attributes that co-occur with one or morequery attributes, a communications subsystem for retrieving theattribute combinations from at least one external database, and a dataprocessing subsystem for comparing and tabulating the attributecombinations. The various subsystems can be discrete components,configurations of electronic circuits within other circuits, softwaremodules running on computing platforms including classes of objects andobject code, or individual commands or lines of code working inconjunction with one or more Central Processing Units (CPUs). A varietyof storage units can be used including but not limited to electronic,magnetic, electromagnetic, optical, opto-magnetic and electro-opticalstorage.

In one application the method and/or system is used in conjunction withone or more databases, such as those that would be maintained byhealth-insurance providers, employers, or health-care providers, whichcan serve to store the aforementioned attribute combinations andcorresponding statistical results. In one embodiment the attributecombinations are stored in a separate dataset from the statisticalresults and the correspondence is achieved using identifiers or keyspresent in (shared across) both datasets. In another embodiment theattribute combinations and corresponding statistical results data isstored with the other attribute data. A user, such as a clinician,physician or patient, can input a query attribute, and that queryattribute can form the basis for tabulating attribute combinationsassociated with that query attribute. In one embodiment the associationswill have been previously stored and are retrieved and displayed to theuser, with the highest ranked (most strongly associated) combinationsappearing first. In an alternate embodiment the tabulation is performedat the time the query attribute is entered, and a threshold can be usedto determine the number of attribute combinations that are to bedisplayed.

FIG. 21 illustrates a flowchart for a method of predictingpredisposition of an individual toward an attribute of interest withwhich they currently have no association or their association iscurrently unknown. In receive query attribute step 2100, query attribute2120 can be provided as one or more attributes in a query by a user.Alternatively, query attribute 2120 can be provided by automatedsubmission, as part of a set of one or more stored attributes that maybe referred to as key attributes. These key attributes can be submittedas a list, or they may be designated attributes within a dataset thatalso contains predisposing attribute combinations with correspondingstatistical results indicating their strength of association with one ormore of the key attributes.

For this example, query attribute ‘A’ is submitted by a user in a query.In access attributes step 2102 the attributes of an individual whoseattribute profile is contained in a 1st dataset 2122 are accessed. Arepresentative 1st dataset for individual #112 is shown in FIG. 22A. Inaccess stored attribute combinations step 2104, attribute combinationsand corresponding statistical results for strength of association withquery attribute 2120 contained in 2nd dataset 2124 are accessed. Arepresentative 2nd dataset for this example is shown in FIG. 22B. Instore the largest attribute combination step 2106, attributecombinations of 2nd dataset 2124 that are also present in 1st dataset2122 are identified by comparison, and the largest identified attributecombination shared by both datasets and its corresponding statisticalresults for strength of association with the query attribute are storedin 3rd dataset 2126 if a minimum statistical requirement for strength ofassociation is met. Absolute risk and relative risk are the preferredstatistical results, although other statistical computations such asodds and odds ratio can also be used. A representative 3rd dataset isshown in FIG. 23A. Individual #112 possesses the largest predisposingattribute combination CEFNTY, for which the corresponding statisticalresults for strength of association with attribute ‘A’ are an absoluterisk of 1.0 and a relative risk of 15.3. In decision step 2108, a choiceis made whether to perform another iteration of steps 2100-2106 foranother attribute of interest. Continuing with this example, attribute‘W’ is received and another iteration is performed. For this example,after completing this iteration there are no additional attributes ofinterest submitted, so upon reaching decision step 2108 the choice ismade not to perform any further iterations. The method concludes withtabulate predisposing attribute combinations step 2110, wherein all or aportion of the data of 3rd dataset 2126 is tabulated to providestatistical predictions for predisposition of the individual toward eachof the query attributes of interest. In one embodiment, the tabulationcan include ordering the tabulated data based on the magnitude of thestatistical results, or the importance of the query attributes.

In one embodiment, the tabulation can be provided in a form suitable forvisual output, such as a visual graphic display or printed report.Attribute combinations do not need to be reported in predispositionprediction and can be omitted or masked so as to provide only the queryattributes of interest and the individual's predisposition predictionfor each. In creating a tabulated report for viewing by a consumer,counselor, agent, physician, patient or consumer, tabulating thestatistical predictions can include substituting the terminology‘absolute risk’ and ‘relative risk’ with the terminology ‘absolutepotential’ and ‘relative potential’, since the term ‘risk’ carriesnegative connotations typically associated with the potential fordeveloping undesirable conditions like diseases. This substitution maybe desirable when the present invention is used to predictpredisposition for desirable attributes such as specific talents orsuccess in careers and sports. Also, the numerical result of absoluterisk is a mathematical probability that can be converted to chance bysimply multiplying it by 100%. It may be desirable to make thisconversion during tabulation since chance is more universally understoodthan mathematical probability. Similarly, relative risk can berepresented as a multiplier, which may facilitate its interpretation.The resulting tabulated results for this example are shown in FIG. 23B,in which all of the aforementioned options for substitution ofterminology and conversion of statistical results have been exercised.The tabulated results of FIG. 23B indicate that individual #112 has a100% chance of having or developing attribute ‘A’ and is 15.3 times aslikely to have or develop attribute ‘A’ as someone in that populationnot associated with attribute combination CEFNTY. The results furtherindicate that individual #112 has a 36% chance of having or developingattribute ‘W’ and is 0.7 times as likely to have or develop attribute‘W’ as someone in that population not associated with attributecombination CE.

In one embodiment a method for individual destiny modification isprovided which accesses a first dataset containing attributes associatedwith an individual and a second dataset containing attributecombinations and statistical computation results that indicate strengthof association of each attribute combination with a query attribute, theattributes being pangenetic, physical, behavioral and situationalattributes. A comparison can be performed to identify the largestattribute combination of the second dataset that consists of attributesof the first dataset. Then, attribute combinations of the second datasetthat either contain that identified attribute combination or consist ofattributes from that identified attribute combination can be stored in athird dataset. The content of the third dataset can be transmitted as atabulation of attribute combinations and corresponding statisticalresults which indicate strengths of association of each attributecombination with the query attribute, thereby providing predispositionpotentials for the individual toward the query attribute givenpossession of those attribute combinations. In one embodiment destinycan be defined as statistical predisposition toward having or acquiringone or more specific attributes.

Similarly, a system can be developed which contains a subsystem foraccessing or receiving a query attribute, a second subsystem foraccessing a dataset containing attributes of an individual, a thirdsubsystem for accessing attribute combinations comprising pangenetic,physical, behavioral, and/or situational attributes that co-occur withone or more query attributes, a communications subsystem for retrievingthe attribute combinations from at least one external database, and adata processing subsystem for comparing and tabulating the attributecombinations. The various subsystems can be discrete components,configurations of electronic circuits within other circuits, softwaremodules running on computing platforms including classes of objects andobject code, or individual commands or lines of code working inconjunction with one or more Central Processing Units (CPUs). A varietyof storage units can be used including but not limited to electronic,magnetic, electromagnetic, optical, opto-magnetic, and electro-opticalstorage.

In one application the method and/or system is used in conjunction withone or more databases, such as those that would be maintained byhealth-insurance providers, employers, or health-care providers, whichcan serve to store the aforementioned attribute combinations andcorresponding statistical results. In one embodiment the attributecombinations are stored in a separate dataset from the statisticalresults and the correspondence is achieved using identifiers or keyspresent in (shared across) both datasets. In another embodiment theattribute combinations and corresponding statistical results data isstored with the other attribute data. A user, such as a clinician,physician or patient, can input a query attribute, and that queryattribute can form the basis for tabulating attribute combinationsassociated with that query attribute. In one embodiment the associationswill have been previously stored and are retrieved and displayed to theuser, with the highest ranked (most strongly associated) combinationsappearing first. In an alternate embodiment the tabulation is performedat the time the query attribute is entered, and a threshold can be usedto determine the number of attribute combinations that are to bedisplayed.

FIG. 24 illustrates a flow chart for a method of providing intelligentdestiny modification in which statistical results for changes to anindividual's predisposition toward a query attribute that result fromthe addition or elimination of specific attribute associations in theirattribute profile are determined. In receive query attribute step 2400,query attribute 2420 can be provided as one or more attributes in aquery by a user or by automated submission. In this example queryattribute ‘A’ is received. In access attributes of an individual step2402, the attribute profile of a selected individual contained in 1stdataset 2422 is accessed. For this example, a representative 1st datasetfor individual #113 is shown in FIG. 25A. In access stored attributecombinations step 2404, attribute combinations from 2nd dataset 2424 andcorresponding statistical results for strength of association with queryattribute 2420 are accessed. FIG. 16 illustrates a representative 2nddataset. In identify the largest attribute combination step 2406, thelargest attribute combination in 2nd dataset 2424 that consists entirelyof attributes present in 1st dataset 2422 is identified by comparison.In this example, the largest attribute combination identified forindividual #113 is CEF. In store attribute combinations step 2408, thoseattribute combinations of 2nd dataset 2424 that either contain thelargest attribute combination identified in step 2406 or consist ofattributes from that attribute combination are selected and stored in3rd dataset 2426. For this example both types of attributes are stored,and the resulting representative 3rd dataset for individual #113 isshown in FIG. 25B. In transmit the attribute combinations step 2410,attribute combinations from 3rd dataset 2426 and their correspondingstatistical results are tabulated into an ordered list of attributecombinations and transmitted as output, wherein the ordering ofcombinations can be based on the magnitudes of the correspondingstatistical results such as absolute risk values. Further, thetabulation may include only a portion of the attribute combinations from3rd dataset 2426 based on subselection. A subselection of attributecombinations that are larger that the largest attribute combinationidentified in step 2406 may require the inclusion of only those thathave at least a minimum statistical association with the queryattribute. For example, a requirement can be made that the largerattribute combinations have an absolute risk value greater than that ofthe attribute combination identified in step 2406. This will ensure theinclusion of only those larger attribute combinations that showincreased predisposition toward the query attribute relative to theattribute combination identified in step 2406. Similarly, a subselectionof attribute combinations that are smaller than the attributecombination identified in step 2406 may require the inclusion of onlythose that have less than a maximum statistical association with thequery attribute. For example, a requirement can be made that the smallerattribute combinations must have an absolute risk less than that of theattribute combination identified in step 2406. This will ensure theinclusion of only those smaller attribute combinations with decreasedpredisposition toward the query attribute relative to the attributecombination identified in step 2406.

In one embodiment the method for individual destiny modification is usedto identify and report attributes that the individual may modify toincrease or decrease their chances of having a particular attribute oroutcome. In one embodiment, the tabulation of attribute combinationsproduced by the method of destiny modification is filtered to eliminatethose attribute combinations that contain one or more attributes thatare not modifiable. In an alternate embodiment, modifiable attributesare prioritized for modification in order to enable efficient destiny(i.e., predisposition) modification. In one embodiment, non-historicalattributes are considered modifiable while historical attributes areconsidered not modifiable. In another embodiment, non-historicalbehavioral attributes are considered to be the most easily or readilymodifiable attributes. In another embodiment, non-historical situationalattributes are considered to be the most easily or readily modifiableattributes. In another embodiment, non-historical physical attributesare considered the most easily or readily modifiable attributes. Inanother embodiment, non-historical pangenetic attributes are consideredthe most easily or readily modifiable attributes. In one embodiment, themodifiable attributes are ranked or otherwise presented in a mannerindicating which are most easily or readily modifiable, which mayinclude creating categories or classes of modifiable attributes, oralternatively, reporting attributes organized according to the attributecategories of the invention.

FIG. 25C illustrates an example of tabulation of attribute combinationsfor individual #113 without statistical subselection of the larger andsmaller attribute combinations. The larger attribute combinations showhow predisposition is altered by adding additional attributes to thelargest attribute combination possessed by individual #113 (bolded), andthe smaller attribute combinations show how predisposition is altered byremoval of attributes from the largest attribute combination possessedby individual.

FIGS. 26A, 26B and 26C illustrate 1st dataset, 3rd dataset and tabulatedresults, respectively, for a different individual, individual #114,processed by the method for destiny modification using the same queryattribute ‘A’ and the 2nd dataset of FIG. 16. The largest attributecombination possessed by individual #114 is CET, which has an absoluterisk of 0.14 for predisposition toward query attribute ‘A’. In thiscase, the tabulation of attribute combinations in FIG. 26C is obtainedby imposing statistical subselection requirements. The subselectionrequired that only those larger attribute combinations having anabsolute risk greater than 0.14 be included and that only those smallerattribute combinations having an absolute risk less than 0.14 beincluded. These subselection requirements result in the exclusion oflarger attribute combination CETY and smaller attribute combination CTfrom the tabulation. In this example, the tabulation also exemplifieshow the nomenclature and statistical computations may be altered toincrease ease of interpretation. Absolute risk results have beenconverted to percentages, relative risk results have been converted tomultipliers, and the terms absolute potential and relative potentialhave been substituted for the terms absolute risk and relative riskrespectively. The tabulated listing of attribute combinations indicateswhat individual #114 can do to increase or decrease their predispositiontoward query attribute ‘A’.

In biological organisms and systems, age and sex type are two somewhatunique and powerful attributes that influence the expression of manyother attributes. For example, age is a primary factor associated with:predicting onset and progression of age-associated diseases in humansand animals; acquiring training and life experiences that lead tosuccess in career, sports and music; and determining life-style choices.Similarly, biological sex type is correlated with profound differencesin expression of physical, behavioral and situational attributes. Theinclusion of accurate data for the age and sex of individuals is veryimportant for acquiring accurate and valid results from the methods ofthe present invention. In one embodiment, specific values of age and sexthat aggregate with a query attribute can be determined by the methodsof the present invention, just as for other attributes, to eitherco-occur or not co-occur in attribute combinations that are associatedwith a query attribute. In one embodiment results of the methods can befiltered according to age and/or sex. In other embodiments a populationor subpopulation can be selected according to age and/or sex(age-matching and/or sex-matching) and then only that subpopulationsubjected to additional processing by methods of the present invention.In another embodiment, an age-matched and/or sex-matched population maybe used to form query-attribute-positive and query-attribute-negativegroups. In another embodiment, the sex and/or age of an individual isused to select a population of age-matched and/or sex-matchedindividuals for creation of an attribute combinations database. Inanother embodiment, the sex and/or age of an individual is used toselect a subpopulation of age-matched and/or sex-matched individuals forcomparison in methods of identifying predisposing attributecombinations, individual predisposition prediction and individualdestiny modification. In another embodiment, summary statistics for ageand/or sex are included with the output results of the methods. Inanother embodiment, summary statistics for age and/or sex are includedwith the output results of the methods when other attributes are omittedor masked for privacy.

Additional embodiments are envisioned which implement a preselection ofindividuals processed by methods of the present invention. In oneembodiment, preselection is a selection or pooling of one or morepopulations or subpopulations of individuals from one or more datasetsor databases based on particular values of attributes such as income,occupation, disease status, zip code or marital status for example.Preselecting populations and subpopulations based on possession of oneor more specified attributes can serve to focus a query on the mostrepresentative population, reduce noise by removing irrelevantindividuals whose attribute data may contribute to increasing error inthe results, and decrease computing time required to execute the methodsby reducing the size of the population to be processed. Also, usingpreselection to define and separate different populations enablescomparison of predisposing attribute combinations toward the same queryattribute between those populations. For example, if two separatesubpopulations are selected—a first population of individuals that earnover $100,000/year and a second population of individuals that earn lessthat $10,000/year—and each subpopulation is processed separately toidentify predisposing attribute combinations for a query attribute ofalcoholism, a comparison of the identities, frequencies of occurrence,and strengths of association of predisposing attribute combinations thatlead to alcoholism in individuals that earn over $100,000 can be madewith those of individuals that earn less than $10,000. In oneembodiment, predisposing attribute combinations that are present in onepreselected population and absent in a second preselected population areidentified. In one embodiment, the frequencies of occurrence and/orstatistical strengths of association of predisposing attributecombinations are compared between two or more preselected populations.In one embodiment, only a single preselected population is selected andprocessed by the methods of the present invention.

Additional embodiments of the methods of the present invention arepossible. In one embodiment, two or more mutually exclusive (having noattributes in common) predisposing attribute combinations for a queryattribute are identified for a single individual and can be tabulatedand presented as output. In one embodiment the query attribute can be anattribute combination, and can be termed a query attribute combination.By submitting a query attribute combination to the methods of thepresent invention, the ability to identify attribute combinations thatpredispose toward other attribute combinations is enabled.

In one embodiment of the methods of the present invention, statisticalmeasures for strength of association of attribute combinations are notstored in a dataset containing the attribute combinations, but rather,are calculated at any time (on as-needed basis) from the frequencies ofoccurrence of the stored attribute combinations. In one embodiment onlya portion of the results from a method of the present invention arepresented, reported or displayed as output. In one embodiment, theresults may be presented as a graphical display or printout includingbut not limited to a 2-dimensional, 3-dimensional or multi-dimensionalaxis, pie-chart, flowchart, bar-graph, histogram, cluster chart,dendrogram, tree or pictogram.

Methods for predisposing attributes identification, predispositionprediction and intelligent destiny modification are subject to error andnoise. A prominent cause of error and noise in methods is bias in theattribute data or in the distribution of the population from which thedata is collected. In one embodiment, bias can manifest as inaccuratefrequencies of occurrence and strengths of association between attributecombinations and query attributes, inaccurate lists of attributesdetermined to co-occur with a query attribute, inaccurate predictions ofan individual's predisposition toward query attributes, and inaccuratelists of modifiable attributes for destiny modification. Bias can resultfrom inaccurate data supplied to methods of the present invention,primarily as a consequence of inaccurate reporting and self-reporting ofattribute data but also as a consequence of collecting attributes frompopulations that are biased, skewed or unrepresentative of theindividual or population for which predisposition predictions aredesired. Error can also result as consequence of faulty attribute datacollection such as misdirected or improperly worded questionnaires.

If bias exists and is left unchecked, it can have different effectsdepending on whether the bias exists with the query attribute, orwhether the bias exists in one or more of the co-occurring attributes ofan attribute combination. At a minimum, the existence of bias in theattribute data or population distribution may result in slightlyinaccurate results for frequency of occurrence of attributes andattribute combinations, and inaccurate statistical strengths ofassociation between attribute combinations and query attributes. Whenbias is present at higher levels, results for frequency of occurrenceand strengths of association can be moderately to highly inaccurate,even producing false positives (Type I Error) and false negatives (TypeII Error), where a false positive is the mistaken identification of anattribute association that actually does not exist (or does not existdifferentially in one population relative to another) and a falsenegative is a mistaken unidentification of an attribute association thatactually does exist (or exists differentially in one population relativeto another).

For the methods described herein, it is possible to minimize error andnoise by ensuring that accurate (unbiased) attribute data are providedto the methods and that representative populations of individuals areused as the basis for creating attribute combination databases. It isanticipated that some degree of inaccuracy of input data will bepresent. The following disclosure indicates sources of error and noiseand ways to identify, avoid and compensate for inaccurate attribute dataand unrepresentative populations.

Selection bias is a major source of error and refers to bias thatresults from using a population of individuals that are notrepresentative of the population for which results and predictions aredesired. For example, if a query for attribute combinations thatpredispose an individual to becoming a professional basketball player isentered against an attributes combination dataset that was created withan over-representation of professional basketball players relative tothe general population, then smaller attribute combinations that areassociated with both professional basketball players and individualsthat are not professional basketball players will receive artificiallyinflated statistical strengths of association with the query attribute,giving a false impression that one needs fewer predisposing attributesthan are actually required to achieve the goal with a high degree ofprobability. Selection bias is largely under the control of thoseresponsible for collecting attribute profiles for individuals of thepopulation and creating datasets that contain that information.Selecting a misrepresentative set of individuals will obviously resultin selection bias as discussed above. Sending questionnaires to arepresentative set of individuals but failing to receive completedquestionnaires from a particular subpopulation, such as a very busygroup of business professionals who failed to take time to fill out andreturn the questionnaire, will also result in selection bias if thereturned questionnaires are used to complete a database without ensuringthat the set of responses are a balanced and representative set for thepopulation as a whole. Therefore, in one embodiment, administrators ofthe methods described herein use a variety of techniques to ensure thatappropriate and representative populations are used so that selectionbias is not present in the attribute profiles and attribute combinationdatasets used as input data for the methods.

Information bias is the second major class of bias and encompasses errordue to inaccuracies in the collected attribute data. The informationbias class comprises several subclasses including misclassificationbias, interview bias, surveillance bias, surrogate interview bias,recall bias and reporting bias.

Misclassification bias refers to bias resulting from misclassifying anindividual as attribute-positive when they are attribute-negative, orvice-versa. To help eliminate this type of bias, it is possible toassign a null for an attribute in circumstances where an accurate valuefor the attribute cannot be ensured.

Interview bias refers to bias resulting from deriving attributes fromquestions or means of information collection that are not correctlydesigned to obtain accurate attribute values. This type of bias isprimarily under the control of those administrators that design andadminister the various modes of attribute collection, and as such, theycan ensure that the means of attribute collection employed are correctlydesigned and validated for collecting accurate values of the targetedattributes.

Surveillance bias refers to bias that results from more closely or morefrequently monitoring one subpopulation of individuals relative toothers, thereby resulting in collection of more accurate and/or morecomplete attribute data for that subpopulation. This is common in casesof individuals suffering from disease, which results in their constantand close monitoring by experienced professionals who may collect moreaccurate and more complete attribute data about many aspects of theindividual, including trivial, routine and common attributes that arenot restricted to the medical field. An administrator of the methodsdescribed herein can seek to reduce this bias by either excludingattribute information obtained as a consequence of surveillance bias orby ensuring that equivalent attribute information is provided for allmembers of the representative population used for the methods.

Surrogate interview bias refers to bias that results from obtaininginaccurate attribute information about an individual from a second-handsource such as a friend or relative. For example, when an individualdies, the only source of certain attribute information may be from aparent or spouse of the individual who may have inaccurate perception ormemory of certain attributes of the deceased individual. To help avoidthis type of bias, it is preferable that a surrogate provider ofattribute information be instructed to refrain from providing attributevalues for which they are uncertain and instead assign a null for thoseattributes.

Recall bias refers to enhanced or diminished memory recall of attributevalues in one subpopulation of individuals versus another. This againmay occur in individuals that are subject to extreme situations such aschronic illness, where the individual is much more conscious andattentive to small details of their life and environment to which otherswould pay little attention and therefore not recall as accurately. Thistype of bias results from inaccuracy in self-reporting and can bedifficult to detect and control for. Therefore, to minimize this type ofbias, it is recommended that attempts to collect self-reported data bemade over a period of time in which individuals are aware of attributesthat are being collected and may even keep a record or journal forattributes that are subject to significant recall bias. Also, whenevermore accurate means than self-reporting can be used to collect attributevalues, the more accurate means should be used.

Reporting bias refers to bias resulting from intentionalmisrepresentation of attribute values. This occurs when individualsunderestimate the value for an attribute or underreport or fail toreport an attribute they perceive as undesirable or are in denial over,or alternatively, when they overestimate the value for an attribute oroverreport or invent possession of an attribute they perceive asdesirable. For example, individuals typically knowingly underestimatethe quantity of alcohol they drink, but overestimate the amount of timethey spend exercising. One approach to encourage accurate self-reportingof attribute values can be to allow the individual to control theirattribute profile record and keep their identity masked or anonymous inresults output or during use of their data by others, when creatingattribute combinations databases for example. If bias can be determinedto exist and quantified at least in relative terms, another approach canbe to use mathematical compensation/correction of the attribute valuereported by the individual by multiplying their reported value by acoefficient or numerical adjustment factor in order to obtain anaccurate value. In one embodiment this type of adjustment can beperformed at the time the data is collected. In another embodiment thistype of adjustment can be performed during conversion and reformattingof data by data conversion/formatting engine 220.

In one embodiment data conversion/formatting engine 220 works toward theremoval of biases by the application of rules which assist in theidentification of biased (suspect) attributes. In one embodiment therules cause the insertion of null attributes where the existingattribute is suspect. In an alternate embodiment, rules are applied toidentify suspect attributes (e.g. overreporting of exercise,underreporting of alcohol consumption) and corrective factors areapplied to those attributes. For example, if it is determined that usersself report consumption of alcohol at about ⅓ the actual rate consumed,the rules can, when attributes are suspect, increase the self-reportedattribute by a factor of 1.5-3.0 depending on how the attribute isbelieved to be suspect. In large databases (e.g. health care databases)the size of the database can be used in conjunction with specificinvestigations (detailed data collection on test groups) to help developrules to both identify and address biases.

In an alternate embodiment, actual possession of attributes and accuratevalues for self-reported attributes are determined using a multiprongeddata collection approach wherein multiple different inquires or means ofattribute collection are used to collect a value for an attribute proneto bias. One example of this approach is to employ a questionnaire thatasks multiple different questions to acquire the same attribute value.For example, if one wants to collect the attribute value for the numberof cigarettes a person smokes each week, a questionnaire can include thefollowing questions which are designed to directly or indirectly acquirethis information: “how many cigarettes do you smoke each day?”, “howmany packs of cigarettes do you smoke each day?”, “how many packs ofcigarettes do you smoke each week?”, “how many packs of cigarettes dopurchase each day? each week?”, “how many cartons of cigarettes do youpurchase each month?”, “how much money do you spend on cigarettes eachday?, each week? each month?”, “how many smoking breaks do you take atwork each day?”. Another example is to ask a person to self-report howmuch time they spend exercising and also collect information from theirgym that shows the time they swipe-in and swipe-out with theirmembership card. In this way, multiple sources of values for anattribute can be obtained and the values compared, cross-validated,deleted, filtered, adjusted, or averaged to help ensure storing accuratevalues for attributes.

In one embodiment the comparison, cross-validation, deletion, filtering,adjusting and averaging of attribute values can be performed duringconversion and reformatting of data by data conversion/formatting engine220. In one embodiment, multiple values obtained for a single attributeare averaged to obtain a final value for the attribute. In oneembodiment, values for an attribute are discarded based on discrepanciesbetween multiple values for an attribute. In one embodiment, one valuefor an attribute is chosen from among multiple values obtained for theattribute based on a comparison of the multiple values. In an alternateembodiment, reported values that appear out of an acceptable range (e.g.statistical outliers) are discarded and the final attribute value isdetermined from the remaining reported values.

Although calculation of the following mathematical measures are notperformed in the examples presented herein, statistical measures ofconfidence including but not limited to variance, standard deviation,confidence intervals, coefficients of variation, correlationcoefficients, residuals, t values (e.g., student's t test, one- andtwo-tailed t-distributions), ANOVA, correlation coefficients (e.g.,regression coefficient, Pearson product-moment correlation coefficient),standard error and p-values can be computed for the results of methodsof the current invention, the computation of which is known to those ofskill in the art. In one embodiment, these confidence measures provide alevel or degree of confidence in the numerical results of the methods sothat the formal, standardized, legal, ethical, business, economic,medical, scientific, or peer-reviewable conclusions and decision-makingcan be made based on the results. In another embodiment, these measuresare computed and compared for frequencies of occurrence of attributecombinations during creation of an attribute combinations database, forexample to determine whether the difference between frequencies ofoccurrence of an attribute combination for the query-attribute-positiveand query-attribute-negative groups is statistically significant for thepurpose, in a further embodiment, of eliminating those attributecombinations that do not have a statistically significant difference infrequency of occurrence between the two populations. Levels ofsignificance and confidence thresholds can be chosen based on userpreference, implementation requirements, or standards of the variousindustries and fields of application.

Aside from the purposes indicated in the above methods, the presentinvention can also be used for investigation of attribute interactionsforming the basis for predisposition. For example, embodiments of themethods can be used to reveal which attributes have diverse andwide-ranging interactions, which attributes have subtle interactions,which attributes have additive effects and which attributes havemultiplicative or exponential synergistic interactions with otherattributes.

In one embodiment, synergistic interactions are particularly importantbecause they have multiplicative or exponential effects onpredisposition, rather than simple additive effects, and can increasepredisposition by many fold, sometimes by as much as 1000 fold. Thesetypes of synergistic interactions are common occurrences in biologicalsystems. For example, synergistic interactions routinely occur withdrugs introduced into biological systems. Depending on thecircumstances, this synergism can lead to beneficial synergisticincreases in drug potency or to synergistic adverse drug reactions.Synergism also occurs in opportunistic infections by microbes. Synergismbetween attributes may also occur in development of physical andbehavioral traits. For example, cigarette smoking and asbestos exposureare known to synergize in multiplicative fashion to cause lung cancer.The same is true for smoking combined with uranium radiation exposure.Exposure to bacterial aflatoxin ingested via farm products combined withchronic hepatitis B infection synergistically causes liver cancer.Revealing synergistic interactions can be invaluable for intelligent andefficient targeting of therapies, treatments, training regimens, andlifestyle alterations to either increase or decrease predispositiontoward an attribute of interest in the most rapid and efficient manner.

FIG. 27A is a representative example of a 3rd dataset resulting from themethod for destiny modification to determine predisposition ofindividual #1 of FIG. 14 toward attribute ‘W’. In contrast, FIG. 27B isa representative example of a 3rd dataset for individual #1 resultingfrom the method for destiny modification to determine predispositiontoward attribute ‘W’ following elimination of attribute ‘A’ from theirattribute profile. By comparing the two datasets, a before and afterlook at the predisposition of individual #1 toward having or developingattribute ‘W’ is provided, where ‘before’ refers to the situation inwhich attribute ‘A’ is still associated with the individual and ‘after’refers to the situation in which attribute ‘A’ is no longer associatedwith the individual. From a comparison of these results, not only is themagnitude of attribute ‘A’ contribution toward predisposition revealed,but synergistic interactions of other attributes with attribute ‘A’ arealso revealed.

In the ‘before’ situation shown in FIG. 27A, the individual possessesthe attribute combination ACE. Addition of association to eitherattribute I, K or Q alone increases absolute risk to 1.0. However, inthe ‘after’ situation of FIG. 27B where the individual begins with thecombination CE, adding association to either attribute I, K or Q alonehas little or no positive effect on predisposition. This reveals that I,K and Q require synergism with A to contribute significantly towardpredisposition to query attribute W in this example. Furthermore,addition of a combination of IQ or IK still has no positive effect onpredisposition in the absence of A. This indicates that I can synergizewith A but not with Q or K. Interestingly, when the combination KQ isadded to the combination CE in the absence of A, absolute risk jumps to1.0. This indicates that K and Q can synergize with each other in thepresence of CE, effectively increasing predisposition to a maximum evenin the absence of attribute A.

In the various embodiments of the present invention, the question as tohow the results are to be used can be considered in the application of aparticular embodiment of the method of attribute identification. Ininstances where the goal is to determine how to reduce predispositiontoward an undesirable attribute for example, then utilizing oneembodiment of the method to determine the identity of predisposingattribute combinations and then proceeding to eliminate an individual'sassociation with those attributes is one way to reduce predispositiontoward that attribute. However, one may also attempt to decreasepredisposition by applying an embodiment of the method to determinethose attribute combinations that are predisposing toward an attributethat is the opposite of the undesirable attribute, and then proceed tointroduce association with those attributes to direct predisposition ofthe individual toward that opposing attribute. In other words, theattributes that predispose toward a key attribute may in many cases notbe simple opposite of attributes that predispose to the opposite of thekey attribute. Approaching this from both angles may provide additionaleffectiveness in achieving the goal of how to most effectively modifypredisposition toward a key attribute of interest. In one embodimentboth approaches are applied simultaneously to increase success inreaching the goal of destiny modification.

Confidentiality of personal attribute data can be a major concern toindividuals that submit their data for analysis. Various embodiments ofthe present invention are envisioned in which the identity of anindividual linked directly or indirectly to their data, or masked, orprovided by privileged access or express permission, including but notlimited to the following embodiments. In one embodiment the identity ofindividuals are linked to their raw attribute profiles. In oneembodiment the identity of individuals are linked directly to their rawattribute profiles. In one embodiment the identity of individuals arelinked indirectly to their raw attribute profiles. In one embodiment theidentity of individuals are anonymously linked to their raw attributeprofiles. In one embodiment the identity of individuals are linked totheir raw attribute profiles using a nondescriptive alphanumericidentifier. In one embodiment the identity of individuals are linked tothe attribute combinations they possess as stored in one or moredatasets of the methods. In one embodiment the linkage of identity isdirect. In one embodiment the linkage of identity is indirect. In oneembodiment the linkage of identity requires anonymizing or masking theidentity of the individual. In one embodiment the linkage of identityrequires use of a nondescriptive alphanumeric identifier.

Various embodiments of the present invention are envisioned in whichdata is made public, or held private, or provided restricted/privilegedaccess granted upon express permission and include but are not limitedto the following embodiments. In one embodiment, the identity ofattributes and statistical results produced in the output of the methodsare provided only to the individual whose attribute profile was accessedfor the query. In one embodiment, the identity of attributes andstatistical results produced in the output of the methods are providedonly to the individual that submitted or authorized the query. In oneembodiment, the identity of attributes and statistical results producedin the output of the methods are provided only to the individualconsumer that paid for the query. In one embodiment, the identity ofattributes and statistical results produced in the output of the methodsare provided only to a commercial organization that submitted,authorized or paid for the query. In one embodiment, the identities ofattributes in the output results from methods of the present inventionare omitted or masked. In one embodiment, the identity of attributes canbe omitted, masked or granted privileged access to by others as dictatedby the individual whose attribute profile was accessed for the query. Inone embodiment, the identity of attributes can be made accessible to agovernment employee, legal professional, medical professional, or otherprofessional legally bound to secrecy. In one embodiment, the identityof attributes can be omitted, masked or granted privileged access to byothers as dictated by a government employee, legal professional, ormedical professional. In one embodiment, the identity of attributes canbe omitted, masked or granted privileged access to by others as dictatedby a commercial organization.

FIG. 28 illustrates a representative computing system on whichembodiments of the present method and system can be implemented. Withrespect to FIG. 28, a Central Processing Unit (CPU) 2800 is connected toa local bus 2802 which is also connected to Random Access Memory (RAM)2804 and disk controller and storage system 2806. CPU 2800 is alsoconnected to an operating system including BIOS 2808 which contains bootcode and which can access disk controller and storage system 2806 toprovide an operational environment and to run an application (e.g.attribute determination). The representative computing system includes agraphics adaptor 2820, display 2830, wireless unit 2840, network adapter2850, LAN 2852, and I/O controller 2810 with printer 2812, mouse 2814,and keyboard 2816.

It will be appreciated by one of skill in the art that the presentmethods, systems, software and databases can be implemented on a numberof computing platforms, and that FIG. 28 is only a representativecomputing platform, and is not intended to limit the scope of theclaimed invention. For example, multiprocessor units with multiple CPUsor cores can be used, as well as distributed computing platforms inwhich computations are made across a network by a plurality of computingunits working in conjunction using a specified algorithm. The computingplatforms may be fixed or portable, and data collection can be performedby one unit (e.g. a handheld unit) with the collected information beingreported to a fixed workstation or database which is formed by acomputer in conjunction with mass storage. Similarly, a number ofprogramming languages can be used to implement the methods and to createthe systems described herein, those programming languages including butnot limited to C, Java, php, C++, perl, visual basic, sql and otherlanguages which can be used to cause the representative computing systemof FIG. 28 to perform the steps described herein.

With respect to FIG. 29, the interconnection of various computingsystems over a network 2900 to realize an attribute determination system800 such as that of FIG. 8, is illustrated. In one embodiment, consumer810 uses a Personal Computer (PC) 2910 to interface with the system andmore specifically to enter and receive data. Similarly, clinician 820uses a workstation 2930 to interface with the system. Genetic databaseadministrator 830 uses an external genetic database 2950 for the storageof genetic/epigenetic data for large populations. Historical,situational, and behavioral data are all maintained on populationdatabase 2960. All of the aforementioned computing systems areinterconnected via network 2900.

In one embodiment, and as illustrated in FIG. 29, an attributedetermination computing and database platform 2940 is utilized to hostthe software-based components of attribute determination system 800, anddata is collected as illustrated in FIG. 8. Once relevant attributes aredetermined, they can be displayed to consumer 810, clinician 820, orboth. In an alternate embodiment, the software-based components ofattribute determination system 800 can reside on workstation 2930operated by clinician 820. Genetic database administrator 830 may alsomaintain and operate attribute determination system 800 and host itssoftware-based components on external genetic database 2950. Anotherembodiment is also possible in which the software-based components ofthe attribute determination system 800 are distributed across thevarious computing platforms. Similarly, other parties and hostingmachines not illustrated in FIG. 29 may also be used to create attributedetermination system 800.

In one embodiment, the datasets of the methods of the present inventionmay be combined into a single dataset. In another embodiment thedatasets may be kept separated. Separate datasets may be stored on asingle computing device or distributed across a plurality of devices.Data, datasets, databases, methods and software of the present inventioncan be embodied on computer-readable media and computer-readable memorydevices.

In one embodiment, at least a portion of the attribute data for one ormore individuals is obtained from medical records. In one embodiment, atleast a portion of the attribute data for one or more individuals isaccessed, retrieved or obtained (directly or indirectly) from acentralized medical records database. In one embodiment, at least aportion of the attribute data for one or more individuals is accessed orretrieved from a centralized medical records database over a computernetwork.

The methods, systems, software and databases described herein have anumber of industrial applications pertaining to the identification ofattributes and combinations of attributes related to a query attribute;creation of databases including the attributes, combinations ofattributes, strength of association with the query attribute, andrankings of strength of association with the query attribute; and use ofthe identified attributes, combinations of attributes, and strength ofassociation of attributes with the query attribute in making a varietyof decisions related to lifestyle, lifestyle modification, diagnosis,medical treatment, eventual outcome (e.g. destiny), possibilities fordestiny modification, and sensitivity analysis (impact or lack thereofof modification of certain attributes).

In one embodiment the methods, system, software, and databases describedherein are used as part of a web based health analysis and diagnosticssystem in which one or more service providers utilize pangeneticinformation (attributes) in conjunction with physical, situational, andbehavioral, attributes to provide services such as longevity analysis,insurance optimization (determination of recommended policies andamounts), and medication impact analysis. In these scenarios, themethods described herein are applied using appropriate query attributesto determine such parameters as the likelihood that the patient willdevelop or has a particular disease, or make an inquiry related tolikelihood of disease development. In one embodiment, the genetic sampleis mailed to an analysis center, where genetic and epigenetic sequencingis performed, and the data stored in an appropriate database. Clinician820 of FIG. 8 or consumer 810 of FIG. 8 provides for reporting of otherdata from which physical, situational, and behavioral attributes aredeveloped and stored. A query related to a diagnosis can be developed byclinician 820 (or other practitioner) and submitted via the web. Usingthe methods and algorithms described herein, a probable diagnosis or setof possible diagnoses can be developed and presented via the webinterface. These diagnoses can be physical or mental. With respect tothe diagnosis of mental illnesses (mental health analyses),identification of key behavioral and situational attributes (e.g.financial attributes, relationship attributes) which may affect mentalhealth is possible using the present methods, systems, software anddatabases. Risk assessments can be performed to indicate what mentalillnesses consumer 810 may be subject to, as well as suggestingmodifications to behavior or living environment to avoid thoseillnesses. For example, a consumer subject to certain types of obsessivedisorders might be advised to change certain behavioral and/orsituational attributes which are associated with that obsessivedisorder, thus decreasing the probability that they will have orexacerbate that disorder.

With respect to general analysis of medical conditions, the web basedsystem can be used to evaluate insurance coverage (amounts and types)and provide recommendations for coverage based on the specific illnessrisks and attributes possessed by the consumer, evaluate the impact (orlack thereof) of lifestyle changes, the impact and effectiveness ofmedications. Such analyses can also be made in view of predispositionpredictions that can indicate probable future development of a disorder,thereby allowing preparations for insurance coverage and therapeuticpreventive measures to be taken in advance of the disorder.

As previously discussed, the system can be used for web based strengthand weakness identification, by allowing the consumer or clinician toquery the system to assess the probability that an individual has aparticular strength or weakness. In one embodiment, parents query thesystem to determine if their child (from which a biological sample wastaken) will have particular strengths (e.g. music or sports) and whatbehavioral attributes should be adopted to maximize the probability ofsuccess at that endeavor, assuming a “natural talent” can be identifiedthrough the combinations of attributes associated with that endeavor.Various service providers, including genetic and epigenetic profilingentities, can interact with the system over a network (e.g., theinternet) and allow the consumer or clinician to interact with thesystem over a network through a web-based interface to obtain thedestiny or attribute information.

In one embodiment a web based goal achievement tool is presented inwhich the consumer enters one or more goals, and the system returnsmodifiable attributes which have been identified using theaforementioned analysis tools, indicating how the consumer can bestobtain the desired goal(s) given their pangenetic, physical,situational, and behavioral makeup.

In one embodiment, potential relationship/life/marriage partners arelocated based on the pangenetic, physical, situational, and behavioralattributes of those individuals, as measured against an attribute modelof a suitable partner developed for the consumer. The attribute model ofthe suitable partner can be developed using a number of techniques,including but not limited to, modeling of partner attributes based onattributes of individuals with which the individual has had previoussuccessful relationships, determination of appropriate “complementary”attributes to the consumer based on statistical studies of individualswith similar attributes to the consumer who are in successfulrelationships and examination of their partner's attributes(determination of appropriate complementary attributes), and an abinitio determination of appropriate partner attributes. Once theattribute model for the most suitable potential partner has beendeveloped, a database containing pangenetic, physical, situational andbehavioral attribute data for potential partners for the consumer can besearched for the purpose of partner identification. In an alternateembodiment a consumer indicates persons they believe have suitablepartner qualities including physical attraction (based on photos orvideo segments) as well as attributes described in profiles associatedwith the persons and their photos. In one embodiment the system usesgenetic and epigenetic information associated with those individuals tocreate a subpopulation of individuals which the consumer believes theyare attracted to, and examines a variety of data associated with thatsubpopulation (e.g., all available attribute data including genetic andepigenetic data) to determine attributes that are indicative ofdesirability to that consumer. In one embodiment the system uses thoseattributes to locate more individuals that could be potentially ofinterest to the consumer and presents those individuals to the consumeras potential partners.

Although the aforementioned methods, systems, software and databaseshave been described as incorporating and utilizing pangenetic, physical,situational and behavioral data, embodiments not utilizing pangeneticinformation are possible, with those embodiments being based solely onphysical, situational and behavioral data. Such embodiments can beutilized to accomplish the tasks described above with respect to theanalysis of biological systems, as well as for the analysis of complexnon-living systems which contain a multitude of attributes. As anexample, a non-biological application of the methodology and systemsdescribed herein would be for the analysis of complex electrical orelectrical-mechanical systems in order to identify probable failuremechanisms (e.g. attributes leading to failure) and as such increasereliability through the identification of those failure-associatedattributes. Additionally, the aforementioned embodiments are based onthe use of information from multiple attribute categories. Embodimentsin which attribute information from a single attribute category(pangenetic, behavioral, physical, or situational) can be used incircumstances where attributes from a single category dominate in thedevelopment of a condition or outcome.

Embodiments of the present invention can be used for a variety ofmethods, databases, software and systems including but not limited to:pattern recognition; feature extraction; binary search trees and binaryprediction tree modeling; decision trees; neural networks andself-learning systems; belief networks; classification systems;classifier-based systems; clustering algorithms; nondeterministicalgorithms (e.g., Monte Carlo methods); deterministic algorithms;scoring systems; decision-making systems; decision-based trainingsystems; complex supervised learning systems; process control systems;chaos analysis systems; interaction, association and correlation mappingsystems; relational databases; navigation and autopilot systems;communications systems and interfaces; career management; job placementand hiring; dating services; marriage counseling; relationshipevaluation; animal companion compatibility evaluation; livingenvironment evaluation; disease and health management and assessment;genetic assessment and counseling; genetic engineering; genetic linkagestudies; genetic screening; genetic drift and evolution discovery;ancestry investigation; criminal investigation; forensics; criminalprofiling; psychological profiling; adoption placement and planning;fertility and pregnancy evaluation and planning; family planning; socialservices; infrastructure planning; species preservation; organismcloning; organism design and evaluation; apparatus design andevaluation; invention design and evaluation; clinical investigation;epidemiological investigation; etiology investigation; diagnosis,prognosis, treatment, prescription and therapy prediction, formulationand delivery; adverse outcome avoidance (i.e. prophylaxis); data mining;bioinformatics; biomarker development; physiological profiling; rationaldrug design; drug interaction prediction; drug screening; pharmaceuticalformulation; molecular modeling; xenobiotic side-effect prediction;microarray analysis; dietary analysis and recommendation; processedfoods formulation; census evaluation and planning; population dynamicsassessment; ecological and environmental preservation; environmentalhealth; land management; agriculture planning; crisis and disasterprediction, prevention, planning and analysis; pandemic and epidemicprediction, prevention, planning and analysis; weather forecasting; goalformulation and goal achievement assessment; risk assessment;formulating recommendations; asset management; task management;consulting; marketing and advertising; cost analysis; businessdevelopment; economics forecasting and planning; stock marketprediction; lifestyle modification; time management; emergencyintervention; operational/failure status evaluation and prediction;system failure analysis; optimization analysis; architectural design;and product appearance, ergonomics, efficiency, efficacy and reliabilityengineering (i.e., product development).

The embodiments of the present invention may be implemented with anycombination of hardware and software. If implemented as acomputer-implemented apparatus, the present invention is implementedusing means for performing all of the steps and functions describedabove.

The embodiments of the present invention can be included in an articleof manufacture (e.g., one or more computer program products) having, forinstance, computer useable media. The media has embodied therein, forinstance, computer readable program code means for providing andfacilitating the mechanisms of the present invention. The article ofmanufacture can be included as part of a computer system or soldseparately.

While specific embodiments have been described in detail in theforegoing detailed description and illustrated in the accompanyingdrawings, it will be appreciated by those skilled in the art thatvarious modifications and alternatives to those details could bedeveloped in light of the overall teachings of the disclosure and thebroad inventive concepts thereof. It is understood, therefore, that thescope of the present invention is not limited to the particular examplesand implementations disclosed herein, but is intended to covermodifications within the spirit and scope thereof as defined by theappended claims and any and all equivalents thereof.

1. A computer, said computer including a display driver device, amemory, a central processor, and computer readable media with executableinstructions thereon, for expanding attribute profiles to increase thestrength of association between a query attribute and a set of attributeprofiles associated with query-attribute-positive individuals,comprising: a) means for receiving a query attribute which identifies,within an initial set of attribute profiles contained in said computermemory, a 1^(st) dataset comprising a set of query-attribute-positiveattribute profiles associated with query-attribute-positive individualsand a set of query-attribute-negative attribute profiles associated withquery-attribute-negative individuals; b) means for identifying a 1^(st)subgroup of query-attribute-positive attribute profiles from the 1^(st)dataset of query-attribute-positive attribute profiles and a 2^(nd)subgroup of query-attribute-negative attribute profiles from the 1^(st)dataset of query-attribute-negative attribute profiles; c) means forgenerating a 1st set of one or more attribute combinations by sequentialcomparison of each of the 1^(st) dataset of query-attribute-positiveattribute profiles with each of the 1^(st) dataset ofquery-attribute-negative attribute profiles; d) means for determining a1^(st) statistical result indicating strength of association of thequery attribute with an attribute combination having a higher frequencyof occurrence in the 1^(st) subgroup of query-attribute-positiveattribute profiles than in the 2^(nd) subgroup ofquery-attribute-negative attribute profiles; e) means for expanding oneor more attributes in the 1^(st) subgroup of query-attribute-positiveattribute profiles and one or more attributes in the 2^(nd) subgroup ofquery-attribute-negative attribute profiles to generate a 3^(rd)subgroup of expanded query-attribute-positive attribute and a 4^(th)subgroup of expanded query-attribute-negative attribute profiles whereinexpanding one or more attributes results in the creation of attributesnot initially contained within the set of attribute profiles; f) meansfor generating a 2^(nd) set of attribute combinations by sequentialcomparison of each of the 3^(rd) subgroup of expandedquery-attribute-positive attribute profiles with each of the 4^(th)subgroup of expanded query-attribute-negative attribute profiles; g)means for determining a 2^(nd) statistical result indicating strength ofassociation of the query attribute with an attribute combination fromthe 2^(nd) set of attribute combinations which has a higher frequency ofoccurrence in the 3^(rd) subgroup of expanded query-attribute-positiveattribute profiles than in the 4^(th) subgroup of expandedquery-attribute-negative attribute profiles; and h) means for storingwithin said computer memory, based on the attribute combination of step(g) when the 2nd statistical result is higher than the 1st statisticalresult, a 3rd set of expanded query-attribute-positive attributeprofiles associated with the query-attribute-positive individuals and a4^(th) set of expanded query-attribute-negative attribute profilesassociated with the query-attribute-negative individuals.
 2. Thecomputer of claim 1, wherein the one or more attributes are primaryattributes, wherein expanding one or more attributes comprises derivingsecondary attributes from the primary attributes, and wherein thesecondary attributes have a lower resolution than the primaryattributes.
 3. The computer of claim 2, wherein said secondary attributeencompasses a primary attribute.
 4. The computer of claim 2, wherein atleast one secondary attribute is derived by compounding the values oftwo or more primary attributes.
 5. The computer of claim 2, wherein atleast one secondary attribute is derived through a heuristic ruleapplied to one or more primary attributes.
 6. The computer of claim 2,wherein at least one secondary attribute comprises an inequalitystatement containing a quantitative value, wherein the quantitativevalue is either larger or smaller than that of the primary attributefrom which it was derived.
 7. The computer of claim 1, wherein theidentity of one or more individuals is masked or anonymized.
 8. Acomputer, said computer including a display driver device, a memory, acentral processor, and computer readable media with executableinstructions thereon, for expanding an attribute profile to increase thestrength of association between a query attribute and the attributeprofile, comprising: a) means for accessing, within said computermemory, an attribute profile containing a set of primary attributeshaving associated time stamps; b) means for generating a set of expandedsecondary attributes from the set of primary attributes based on acalculation relating to the time stamps associated with the primaryattributes and a primary attribute itself, wherein the set of expandedsecondary attributes resulting from the calculation were not containedwithin the primary attribute profile and result in a higher strength ofassociation between a query attribute and an attribute profilecontaining the set of expanded secondary attributes than between thequery attribute and an attribute profile containing only the set ofprimary attributes; and c) means for storing, within said computermemory, the set of secondary attributes in association with theattribute profile to create an expanded attribute profile.
 9. Thecomputer of claim 8, further comprising: d) determining the strength ofassociation of the expanded attribute profile with a query attribute bycomparing the expanded attribute profile to a set of attributecombinations that are statistically associated with the query attribute.10. The computer of claim 8, further comprising: d) determiningattribute combinations that are associated with a query attribute byidentifying attribute combinations from the expanded attribute profilethat have higher frequencies of occurrence in a set of attributeprofiles associated with query attribute-positive individuals than in aset of attribute profiles associated with query attribute-negativeindividuals.
 11. The computer of claim 8, wherein the set of secondaryattributes has lower resolution than the set of primary attributes. 12.The computer of claim 8, wherein a secondary attribute encompasses aprimary attribute.
 13. The computer of claim 8, wherein at least onesecondary attribute in the set of secondary attributes is a categoricalattribute that encompasses at least one primary attribute in the set ofprimary attributes that is a numerical attribute.
 14. The computer ofclaim 8, wherein at least one secondary attribute is derived bycompounding the values of two or more primary attributes.
 15. Thecomputer of claim 8, wherein at least one primary attribute has acontinuous value and at least one secondary attribute derived from thatprimary attribute has a discrete value.
 16. The computer of claim 8,wherein at least one secondary attribute is derived through a heuristicrule applied to one or more primary attributes.
 17. The computer ofclaim 8, wherein at least one secondary attribute comprises aninequality statement containing a quantitative value, wherein thequantitative value is either larger or smaller than that of the primaryattribute from which it was derived.
 18. The computer of claim 8,wherein two or more of the secondary attributes comprise a sequence ofinequality statements containing progressively larger quantitativevalues.
 19. The computer of claim 8, wherein two or more of thesecondary attributes comprise a sequence of inequality statementscontaining progressively smaller quantitative values.
 20. A computersystem, including a data entry device, a display driver device, amemory, a central processor, and computer readable media with executableinstructions thereon for operating a machine for expanding attributeprofiles to increase the strength of association between a queryattribute and a set of attribute profiles associated withquery-attribute-positive individuals, comprising: a) a data receivingsubsystem means for receiving a query attribute through said data entrydevice; b) a data accessing subsystem means for accessing in saidmemory: i) a set of attribute profiles associated with a group ofquery-attribute-positive individuals; ii) a set of attribute profilesassociated with a group of query-attribute-negative individuals; c) adata processing subsystem comprising a data conversion subsystem meansset forth in said computer readable media for expanding one or moreattributes in the set of attribute profiles associated with the group ofquery-attribute-positive individuals and one or more attributes in theset of attribute profiles associated with the query-attribute-negativeindividuals to create a set of expanded attribute profiles associatedwith the group of query-attribute-positive individuals and a set ofexpanded attribute profiles associated with the group ofquery-attribute-negative individuals, wherein neither set of expandedprofiles existed in the original attribute profiles; d) a statisticalcomputation subsystem means set forth in said computer readable mediafor: i) calculating a first statistical result indicating strength ofassociation of the query attribute with an attribute combination havinga higher frequency of occurrence in the set of attribute profilesassociated with the group of query-attribute-positive individuals thanin the set of attribute profiles associated with the group of queryattribute-negative individuals; ii) calculating a second statisticalresult indicating strength of association of the query attribute with anattribute combination having a higher frequency of occurrence in the setof expanded attribute profiles associated with the group ofquery-attribute-positive individuals than in the set of expandedattribute profiles associated with the group of query-attribute-negativeindividuals; and e) a data storage subsystem means set forth in saidcomputer readable media for storing, when the second statistical resultis higher than the first statistical result, the expanded attributeprofiles associated with the group of query-attribute-positiveindividuals and the expanded attribute profiles associated with thegroup of query-attribute-negative individuals.